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Safder, Usman,Ifaei, Pouya,Yoo, ChangKyoo Elsevier 2018 Energy conversion and management Vol.166 No.-
<P><B>Abstract</B></P> <P>In the present study, an optimal power and freshwater cogeneration system is proposed to meet the global requirements sustainably. A Rankine cycle (RC), an organic Rankine cycle (ORC) and a reverse osmosis (RO) module are integrated to form the proposed system. The performance of the system is investigated using thermo-mathematical models allocating seven organic fluids in the bottoming ORC. A novel evolutionary algorithm-based multi-objective optimization approach is applied using thermorisk and thermoeconomic analyses. Thus, an optimal configuration is determined at both global and local scales. Finally, a flexibility analysis is performed to the optimal configuration considering probable uncertainties in the market. The optimization results showed that the total accidental risk impact and the total product cost rate improved by 2.49–48.73% and 5.67–62.41%, respectively, depending on the employed organic fluid. The highest exergetic efficiency and the minimum specific power consumption were obtained as 52.74% and 4.111 kWh/m<SUP>3</SUP>, allocating R245fa in the optimal system. The system enjoying R123 had the widest flexibility range without any increases in the optimum total product costs.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A cogeneration system is optimized decreasing thermoeconomic and thermorisk impact. </LI> <LI> The system’s flexibility is analyzed investigating the uncertainties of the market. </LI> <LI> Various cases are studied with respect to the properties of working organic fluids. </LI> <LI> The cost product rate and specific risk improved by 62.41% and 48.73%, respectively. </LI> <LI> The highest exergetic efficiency was obtained to be 52.74% using R245fa. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
Safder, Usman,Nam, KiJeon,Kim, Dongwoo,Shahlaei, Mohsen,Yoo, ChangKyoo Elsevier 2018 Ecotoxicology and environmental safety Vol.162 No.-
<P><B>Abstract</B></P> <P>Octanol/water partition coefficient (log P), octanol/air partition coefficient (log K<SUB>OA</SUB>) and bioconcentration factor (log BCF) are important physiochemical properties of organic substances. Quantitative structure-property relationship (QSPR) models are a promising alternative method of reducing and replacing experimental steps in determination of log P, log K<SUB>OA</SUB> and log BCF. In the current study, we propose a new QSPR model based on a deep belief network (DBN) to predict the physicochemical properties of polychlorinated biphenyls (PCBs). The prediction accuracy of the proposed model was compared to the results of previous reported models. The predictive ability of the DBN model, validated with a test set, is clearly superior to the other models. All results showed that the proposed model is robust and satisfactory, and can effectively predict the physiochemical properties of PCBs without highly reliable experimental values.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A QSPR network to improve the prediction of biological activity of PCBs by 3–13%. </LI> <LI> DBN is suggested to give better prediction of log P, log BCF and log K<SUB>OA</SUB>. </LI> <LI> Appropriate for high throughput, unlike previously reported methods. </LI> <LI> The proposed model has better R<SUP>2</SUP> <SUB>pred</SUB> and PRESS compared with three reported models. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
Safder, Usman,Nam, KiJeon,Kim, Dongwoo,Heo, SungKu,Yoo, ChangKyoo Elsevier 2019 Ecotoxicology and environmental safety Vol.169 No.-
<P><B>Abstract</B></P> <P>A fine particulate matter less than 2.5 µm (PM<SUB>2.5</SUB>) in the underground subway system are the cause of many diseases. The iron containing PMs frequently confront in underground stations, which ultimately have an impact on the health of living beings especially in children. Hence, it is necessary to conduct toxicity assessment of chemical species and regularized the indoor air pollutants to ensure the good health of children. Therefore, in this study, a new indoor air quality (IAQ) index is proposed based on toxicity assessment by quantitative structure-activity relationship (QSAR) model. The new indices called comprehensive indoor air toxicity (CIAT) and cumulative comprehensive indoor air toxicity (CCIAT) suggests the new standards based on toxicity assessment of PM<SUB>2.5</SUB>. QSAR based deep neural network (DNN) exhibited the best model in predicting the toxicity assessment of chemical species in particulate matters, which yield lowest RMSE and Q F 3 2 values of 0.6821 and 0.8346, respectively, in the test phase. After integration with a standard concentration of PM<SUB>2.5</SUB>, two health risk indices of CIAT and CCIAT are introduced based on toxicity assessment results, which can be use as the toxicity standard of PM<SUB>2.5</SUB> for detail IAQ management in a subway station. These new health risk indices suggest more sensitive air pollutant level of iron containing fine particulate matters or molecular level contaminants in underground spaces, alerting the health risk of adults and children in “unhealthy for sensitive group”.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A QSAR based toxicity assessment of PM<SUB>2.5</SUB> for detail IAQ management. </LI> <LI> DNN-based QSAR model for toxicity assessment of PM<SUB>2.5</SUB> shows better performance. </LI> <LI> Suggested two new sensitive IAQ monitoring standards, CIAT and CCIAT. </LI> <LI> PM<SUB>2.5</SUB> are partially observed high concentrations to children 41–74 mg/m<SUP>3</SUP> air. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
Heo, SungKu,Safder, Usman,Yoo, ChangKyoo Elsevier 2019 Environmental pollution Vol.253 No.-
<P><B>Abstract</B></P> <P>Over 80,000 endocrine-disrupting chemicals (EDCs) are considered emerging contaminants (ECs), which are of great concern due to their effects on human health. Quantitative structure-activity relationship (QSAR) models are a promising alternative to <I>in vitro</I> methods to predict the toxicological effects of chemicals on human health. In this study, we assessed a deep-learning based QSAR (DL-QSAR) model to predict the qualitative and the quantitative effects of EDCs on the human endocrine system, and especially sex-hormone binding globulin (SHBG) and estrogen receptor (ER). Statistical analyses of the qualitative responses indicated that the accuracies of all three DL-QSAR methods were above 90%, and greater than the other statistical and machine learning models, indicating excellent classification performance. The quantitative analyses, as assessed using deep-neural-network-based QSAR (DNN-QSAR), resulted in a coefficient of determination (R<SUP>2</SUP>) of 0.80 and predictive square correlation coefficient (Q<SUP>2</SUP>) of 0.86, which implied satisfactory goodness of fit and predictive ability. Thus, DNN was able to transform sparse molecular descriptors into higher dimensional spaces, and was superior for assessment qualitative responses. Moreover, DNN-QSAR demonstrated excellent performance in the discipline of computational chemistry by handling multicollinearity and overfitting problems.</P> <P><B>Highlights</B></P> <P> <UL> <LI> VIP and LASSO regression were implemented to select key molecular descriptors. </LI> <LI> DL-QSAR model was used to predict the responses of EDCs to SHBG and ER. </LI> <LI> DNN-QSAR model obtained Q<SUP>2</SUP> of 0.86 prediction performance. </LI> <LI> DNN-QSAR model showed accuracy of 97.0% for classification performance. </LI> <LI> Model performance of DL-QSAR models outperformed other conventional ML techniques. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
Hemeoxygenase-1 maintains bone mass via attenuating a redox imbalance in osteoclast
Ke, K.,Safder, M.A.,Sul, O.J.,Kim, W.K.,Suh, J.H.,Joe, Y.,Chung, H.T.,Choi, H.S. North-Holland 2015 Molecular and cellular endocrinology Vol.409 No.-
Heme oxygenase-1 (HO-1) has long been considered to be an endogenous antioxidant. However, the role of HO-1 is highly controversial in developing metabolic diseases. We hypothesized that HO-1 plays a role in maintaining bone mass by alleviating a redox imbalance. We investigated its role in bone remodeling. The absence of HO-1 in mice led to decreased bone mass with elevated activity and number of OCs, as well as higher serum levels of reactive oxygen species (ROS). HO-1, which is constitutively expressed at a high level in osteoclast (OC) precursors, was down-regulated during OC differentiation. HO-1 deficiency in bone marrow macrophages (BMM) in vitro resulted in increased numbers and activity of OCs due to enhanced receptor activator of nuclear factor-κB ligand (RANKL) signaling. This was associated with increased activation of nuclear factor-κB and of nuclear factor of activated T-cells, cytoplasmic 1 along with elevated levels of intracellular calcium and ROS. Decreased bone mass in the absence of HO-1 appears to be mainly due to increased osteoclastogenesis and bone resorption resulting from elevated RANKL signaling in OCs. Our data highlight the potential role of HO-1 in maintaining bone mass by negatively regulating OCs.