Gender differences in the relationship between loneliness and health-related behavioral risk factors among the Hakka elderly in Fujian, China

IntroductionTo explore gender differences in the relationship between loneliness and health-related behavioral risk factors (BRFs) among the Hakka elderly.MethodsLoneliness was measured by the UCLA Loneliness Scale Short-form (ULS-8). Seven BRFs were examined. Mann–Whitney U, Kruskal-Wallis, and post hoc tests were conducted to compare the differences in ULS-8 scores among the Hakka elderly with different BRFs. Generalized linear regression models were employed to examine the associations of specific BRF and its number with the ULS-8 scores among the Hakka elderly in male, female, and total samples.ResultsPhysical inactivity (B = 1.96, p < 0.001), insufficient leisure activities participation (B = 1.44, p < 0.001), unhealthy dietary behavior (B = 1.02, p < 0.001), and irregular sleep (B = 2.45, p < 0.001) were positively correlated with the ULS-8 scores, whereas drinking (B = −0.71, p < 0.01) was negatively associated with the ULS-8 scores in the total sample. In males, insufficient leisure activities participation (B = 2.35, p < 0.001), unhealthy dietary behavior (B = 1.39, p < 0.001), and irregular sleep (B = 2.07, p < 0.001) were positively associated with the ULS-8 scores. In females, physical inactivity (B = 2.69, p < 0.001) and irregular sleep (B = 2.91, p < 0.001) was positively correlated with the scores of ULS-8, while drinking (B = −0.98, p < 0.05) was negatively associated with the ULS-8 scores. More BRFs were significantly related to greater loneliness (p < 0.001).ConclusionThere are gender differences in the relationship between loneliness and BRFs among the Hakka elderly, and individuals with more BRFs were more likely to feel loneliness. Therefore, the co-occurrence of multiple BRFs requires more attention, and integrated behavioral intervention strategies should be adopted to reduce the loneliness of the elderly

Mental health status and associated contributing factors among the Hakka elderly in Fujian, China

PurposeLittle is known about the mental health of the Hakka elderly. This study explores the status of, and factors associated with mental health among Hakka elderly populations from Fujian, China.MethodsThis is a cross-sectional, community-based survey study containing a total of 1,262 valid samples. The Chinese version Symptom Checklist-90-R (SCL-90-R) was used to assess the mental health status of the Hakka elderly. We used t-tests to compare the differences for 10 dimensions of SCL-90-R scores between the Chinese national norm and the Hakka elderly. Univariate and multivariate analysis were performed by using linear regression analysis to identify the main socio-demographic factors that were most predictive of the total score of SCL-90-R in the Hakka elderly.ResultsThe scores of somatization (1.78 ± 0.55 vs. 1.40 ± 0.46, P < 0.001) and phobic anxiety (1.21 ± 0.36 vs. 1.17 ± 0.31, P < 0.001) for the Hakka elderly in Fujian appeared to be significantly higher than the Chinese norm. The higher total scores of SCL-90-R were found among females (β = 0.030, P = 0.044), widowed persons (β = 0.053, P = 0.021), those with parent(s) alive (β = 0.047, P = 0.019), and those with poorer self-rated health status (β = 0.110, P < 0.001). The lower total scores of SCL-90-R were found among those who were currently living in town, those with lower education level, those with higher average annual household incomes, and those who were living with spouse or children.ConclusionThe worse mental health conditions of the Hakka elderly in somatization and phobic anxiety were detected. The overall mental health status was shown to be worse among females, widowed persons, those who were living in village, those with lower education, and those with father or/and mother alive

Addendum: Shi, X.D.; Ruan, W.Q.; Hu, J.W.; Fan, M.Y.; Cao, R.S.; Wei, X.H. Optimizing the Removal of Rhodamine B in Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zerovalent Iron (nZVI/rGO) Using an Artificial Neural Network-Genetic Algorithm (ANN-GA). Nanomaterials 2017, 7, 134

The authors wish to make the following addendum to their paper [1]:[...

Theoretical Studies on Structures, Properties and Dominant Debromination Pathways for Selected Polybrominated Diphenyl Ethers

The B3LYP/6-311+G(d)-SDD method, which considers the relativistic effect of bromine, was adopted for the calculations of the selected polybrominated diphenyl ethers (PBDEs) in the present study, in which the B3LYP/6-311+G(d) method was also applied. The calculated values and experimental data for structural parameters of the selected PBDEs were compared to find the suitable theoretical methods for their structural optimization. The results show that the B3LYP/6-311+G(d) method can give the better results (with the root mean square errors (RMSEs) of 0.0268 for the C–Br bond and 0.0161 for the C–O bond) than the B3LYP/6-311+G(d)-SDD method. Then, the B3LYP/6-311+G(d) method was applied to predict the structures for the other selected PBDEs (both neutral and anionic species). The lowest unoccupied molecular orbital (LUMO) and the electron affinity are of a close relationship. The electron affinities (vertical electron affinity and adiabatic electron affinity) were discussed to study their electron capture abilities. To better estimate the conversion of configuration for PBDEs, the configuration transition states for BDE-5, BDE-22 and BDE-47 were calculated at the B3LYP/ 6-311+G(d) level in both gas phase and solution. The possible debromination pathway for BDE-22 were also studied, which have bromine substituents on two phenyl rings and the bromine on meta-position prefers to depart from the phenyl ring. The reaction profile of the electron-induced reductive debromination for BDE-22 were also shown in order to study its degradation mechanism

Optimizing the Removal of Rhodamine B in Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zerovalent Iron (nZVI/rGO) Using an Artificial Neural Network-Genetic Algorithm (ANN-GA)

Rhodamine B (Rh B) is a toxic dye that is harmful to the environment, humans, and animals, and thus the discharge of Rh B wastewater has become a critical concern. In the present study, reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) was used to treat Rh B aqueous solutions. The nZVI/rGO composites were synthesized with the chemical deposition method and were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectroscopy, N2-sorption, and X-ray photoelectron spectroscopy (XPS) analysis. The effects of several important parameters (initial pH, initial concentration, temperature, and contact time) on the removal of Rh B by nZVI/rGO were optimized by response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA). The results suggest that the ANN-GA model was more accurate than the RSM model. The predicted optimum value of Rh B removal efficiency (90.0%) was determined using the ANN-GA model, which was compatible with the experimental value (86.4%). Moreover, the Langmuir, Freundlich, and Temkin isotherm equations were applied to fit the adsorption equilibrium data, and the Freundlich isotherm was the most suitable model for describing the process for sorption of Rh B onto the nZVI/rGO composites. The maximum adsorption capacity based on the Langmuir isotherm was 87.72 mg/g. The removal process of Rh B could be completed within 20 min, which was well described by the pseudo-second order kinetic model

Optimizing Low-Concentration Mercury Removal from Aqueous Solutions by Reduced Graphene Oxide-Supported Fe3O4 Composites with the Aid of an Artificial Neural Network and Genetic Algorithm

Reduced graphene oxide-supported Fe3O4 (Fe3O4/rGO) composites were applied in this study to remove low-concentration mercury from aqueous solutions with the aid of an artificial neural network (ANN) modeling and genetic algorithm (GA) optimization. The Fe3O4/rGO composites were prepared by the solvothermal method and characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), atomic force microscopy (AFM), N2-sorption, X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR) and superconduction quantum interference device (SQUID). Response surface methodology (RSM) and ANN were employed to model the effects of different operating conditions (temperature, initial pH, initial Hg ion concentration and contact time) on the removal of the low-concentration mercury from aqueous solutions by the Fe3O4/rGO composites. The ANN-GA model results (with a prediction error below 5%) show better agreement with the experimental data than the RSM model results (with a prediction error below 10%). The removal process of the low-concentration mercury obeyed the Freudlich isotherm and the pseudo-second-order kinetic model. In addition, a regeneration experiment of the Fe3O4/rGO composites demonstrated that these composites can be reused for the removal of low-concentration mercury from aqueous solutions

Removal of Crystal Violet by Using Reduced-Graphene-Oxide-Supported Bimetallic Fe/Ni Nanoparticles (rGO/Fe/Ni): Application of Artificial Intelligence Modeling for the Optimization Process

Reduced-graphene-oxide-supported bimetallic Fe/Ni nanoparticles were synthesized in this study for the removal of crystal violet (CV) dye from aqueous solutions. This material was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) coupled with energy dispersive spectroscopy (EDS), Raman spectroscopy, N2-sorption, and X-ray photoelectron spectroscopy (XPS). The influence of independent parameters (namely, initial dye concentration, initial pH, contact time, and temperature) on the removal efficiency were investigated via Box–Behnken design (BBD). Artificial intelligence (i.e., artificial neural network, genetic algorithm, and particle swarm optimization) was used to optimize and predict the optimum conditions and obtain the maximum removal efficiency. The zero point of charge (pHZPC) of rGO/Fe/Ni composites was determined by using the salt addition method. The experimental equilibrium data were fitted well to the Freundlich model for the evaluation of the actual behavior of CV adsorption, and the maximum adsorption capacity was estimated as 2000.00 mg/g. The kinetic study discloses that the adsorption processes can be satisfactorily described by the pseudo-second-order model. The values of Gibbs free energy change (ΔG0), entropy change (ΔS0), and enthalpy change (ΔH0) demonstrate the spontaneous and endothermic nature of the adsorption of CV onto rGO/Fe/Ni composites

Artificial Intelligence Based Optimization for the Se(IV) Removal from Aqueous Solution by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron Composites

Highly promising artificial intelligence tools, including neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), were applied in the present study to develop an approach for the evaluation of Se(IV) removal from aqueous solutions by reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites. Both GA and PSO were used to optimize the parameters of ANN. The effect of operational parameters (i.e., initial pH, temperature, contact time and initial Se(IV) concentration) on the removal efficiency was examined using response surface methodology (RSM), which was also utilized to obtain a dataset for the ANN training. The ANN-GA model results (with a prediction error of 2.88%) showed a better agreement with the experimental data than the ANN-PSO model results (with a prediction error of 4.63%) and the RSM model results (with a prediction error of 5.56%), thus the ANN-GA model was an ideal choice for modeling and optimizing the Se(IV) removal by the nZVI/rGO composites due to its low prediction error. The analysis of the experimental data illustrates that the removal process of Se(IV) obeyed the Langmuir isotherm and the pseudo-second-order kinetic model. Furthermore, the Se 3d and 3p peaks found in XPS spectra for the nZVI/rGO composites after removing treatment illustrates that the removal of Se(IV) was mainly through the adsorption and reduction mechanisms

Modeling of Malachite Green Removal from Aqueous Solutions by Nanoscale Zerovalent Zinc Using Artificial Neural Network

The commercially available nanoscale zerovalent zinc (nZVZ) was used as an adsorbent for the removal of malachite green (MG) from aqueous solutions. This material was characterized by X-ray diffraction and X-ray photoelectron spectroscopy. The advanced experimental design tools were adopted to study the effect of process parameters (viz. initial pH, temperature, contact time and initial concentration) and to reduce number of trials and cost. Response surface methodology and rapidly developing artificial intelligence technologies, i.e., artificial neural network coupled with particle swarm optimization (ANN-PSO) and artificial neural network coupled with genetic algorithm (ANN-GA) were employed for predicting the optimum process variables and obtaining the maximum removal efficiency of MG. The results showed that the removal efficiency predicted by ANN-GA (94.12%) was compatible with the experimental value (90.72%). Furthermore, the Langmuir isotherm was found to be the best model to describe the adsorption of MG onto nZVZ, while the maximum adsorption capacity was calculated to be 1000.00 mg/g. The kinetics for adsorption of MG onto nZVZ was found to follow the pseudo-second-order kinetic model. Thermodynamic parameters (ΔG0, ΔH0 and ΔS0) were calculated from the Van’t Hoff plot of lnKc vs. 1/T in order to discuss the removal mechanism of MG