Psychache status and associated contributing factors among the Hakka elderly in Fujian, China.

BACKGROUND: Little is known about the state of psychological distress of the elderly in China, and research on specific subgroups such as Hakka older adults is almost lacking. This study investigates psychache and associated factors among Hakka elderly in Fujian, China. METHODS: The data analysed in this study were derived from China's Health-Related Quality of Life Survey for Older Adults 2018. The Chinese version of the Psychache Scale (PAS) was used to assess the frequency and intensity of psychache in Hakka older adults. Generalized linear regression analysis was conducted to identify the main socio-demographic factors associated with psychache overall and its frequency and intensity. RESULTS: A total of 1,262 older adults participated, with mean scores of 18.27 ± 6.88 for total PAS, 12.50 ± 4.79 for PAS-Frequency and 5.77 ± 2.34 for PAS-Intensity. On average, females scored higher than males on PAS-Frequency (ß = 0.84, 95% CI = 0.34, 1.35) and PAS-Intensity (ß = 0.48, 95% CI = 0.22, 0.73). Older adults currently living in towns (ß = -2.18, 95% CI = -2.81, -1.54), with their spouse only (ß = -3.71, 95% CI = -4.77, -2.65), or with children (ß = -3.24, 95% CI = -4.26, -2.22) were more likely to score lower on PAS-Frequency. Conversely, older adults who were regular sleepers (ß = -1.19, 95% CI =-1.49, -0.88) or lived with their spouse only (ß = -1.25, 95% CI = -1.78, -0.72) were more likely to score lower on PAS-Intensity. CONCLUSION: Among Hakka elderly, we found a higher frequency and greater intensity of psychache in females, those with poor health status, irregular sleepers, rural residents, solo dwellers, those with below CNY 10,000 in personal savings, and the medically uninsured. The study's findings indicate that policymakers should give more attention to the susceptible population and implement practical interventions to reduce their psychological burden.

Socio-Demographic Factors Associated with Rural Residents' Dietary Diversity and Dietary Pattern: A Cross-Sectional Study in Pingnan, China.

There is limited evidence regarding the factors correlated with dietary diversity (DD) and dietary pattern (DP) in rural residents of China. This study aims to identify the DD and DP of rural residents and their association with socio-demographic factors. A cross-sectional survey was conducted in Pingnan, China. The Food Frequency Questionnaire (FFQ) was applied to evaluate dietary intake. Latent class analysis (LCA) was used to identify patterns of six food varieties, including vegetables-fruits, red meat, aquatic products, eggs, milk, and beans-nuts. Generalized linear models and multiple logistic regression models were used to determine factors associated with the DD and DP. Three DPs were detected by LCA, namely "healthy" DP (47.94%), "traditional" DP (33.94%), and "meat/animal protein" DP (18.11%). Females exhibited lower DD (ß = -0.23, p = 0.003) and were more likely to adhere to "traditional" DP (OR = 1.46, p = 0.039) and "meat/animal protein" DP (OR = 2.02, p < 0.001). Higher educational levels and annual household income (AHI) were positively associated with higher DD (p < 0.05) and less likely to have "traditional" DP and "meat/animal protein" DP (p < 0.05). Non-obese people exhibited higher DD (ß = 0.15, p = 0.020) and were less likely to have "meat/animal protein" DP (OR = 0.59, p = 0.001). Our study reveals that females, those with lower educational levels and AHI, and obese people are more likely to have a lower DD and are more likely to adhere to "traditional" DP and "meat/animal protein" DP. The local, regional, and even national performance of specific diet-related health promotion measures and interventions must target these vulnerable populations to improve a healthier DD and DP.

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

Introduction: To explore gender differences in the relationship between loneliness and health-related behavioral risk factors (BRFs) among the Hakka elderly. Methods: Loneliness 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. Results: Physical 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). Conclusion: There 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.

Purpose: Little 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. Methods: This 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. Results: The 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 (ß = 0.030, P = 0.044), widowed persons (ß = 0.053, P = 0.021), those with parent(s) alive (ß = 0.047, P = 0.019), and those with poorer self-rated health status (ß = 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. Conclusion: The 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.

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 (ΔG°), entropy change (ΔS°), and enthalpy change (ΔH°) 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.

A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence.

Water pollution occurs mainly due to inorganic and organic pollutants, such as nutrients, heavy metals and persistent organic pollutants. For the modeling and optimization of pollutants removal, artificial intelligence (AI) has been used as a major tool in the experimental design that can generate the optimal operational variables, since AI has recently gained a tremendous advance. The present review describes the fundamentals, advantages and limitations of AI tools. Artificial neural networks (ANNs) are the AI tools frequently adopted to predict the pollutants removal processes because of their capabilities of self-learning and self-adapting, while genetic algorithm (GA) and particle swarm optimization (PSO) are also useful AI methodologies in efficient search for the global optima. This article summarizes the modeling and optimization of pollutants removal processes in water treatment by using multilayer perception, fuzzy neural, radial basis function and self-organizing map networks. Furthermore, the results conclude that the hybrid models of ANNs with GA and PSO can be successfully applied in water treatment with satisfactory accuracies. Finally, the limitations of current AI tools and their new developments are also highlighted for prospective applications in the environmental protection.

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.

Artificial Neural Network Modeling and Genetic Algorithm Optimization for Cadmium Removal from Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron (nZVI/rGO) Composites.

Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites were synthesized in the present study by chemical deposition method and were then characterized by various methods, such as Fourier-transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The nZVI/rGO composites prepared were utilized for Cd(II) removal from aqueous solutions in batch mode at different initial Cd(II) concentrations, initial pH values, contact times, and operating temperatures. Response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA) were used for modeling the removal efficiency of Cd(II) and optimizing the four removal process variables. The average values of prediction errors for the RSM and ANN-GA models were 6.47% and 1.08%. Although both models were proven to be reliable in terms of predicting the removal efficiency of Cd(II), the ANN-GA model was found to be more accurate than the RSM model. In addition, experimental data were fitted to the Langmuir, Freundlich, and Dubinin-Radushkevich (D-R) isotherms. It was found that the Cd(II) adsorption was best fitted to the Langmuir isotherm. Examination on thermodynamic parameters revealed that the removal process was spontaneous and exothermic in nature. Furthermore, the pseudo-second-order model can better describe the kinetics of Cd(II) removal with a good R² value than the pseudo-first-order model.

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.

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.

Synthesis and Characterization of Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron (nZVI/rGO) Composites Used for Pb(II) Removal.

Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites were prepared by chemical deposition method and were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectroscopy, N2-sorption and X-ray photoelectron spectroscopy (XPS). Operating parameters for the removal process of Pb(II) ions, such as temperature (20-40 °C), pH (3-5), initial concentration (400-600 mg/L) and contact time (20-60 min), were optimized using a quadratic model. The coefficient of determination (R² > 0.99) obtained for the mathematical model indicates a high correlation between the experimental and predicted values. The optimal temperature, pH, initial concentration and contact time for Pb(II) ions removal in the present experiment were 21.30 °C, 5.00, 400.00 mg/L and 60.00 min, respectively. In addition, the Pb(II) removal by nZVI/rGO composites was quantitatively evaluated by using adsorption isotherms, such as Langmuir and Freundlich isotherm models, of which Langmuir isotherm gave a better correlation, and the calculated maximum adsorption capacity was 910 mg/g. The removal process of Pb(II) ions could be completed within 50 min, which was well described by the pseudo-second order kinetic model. Therefore, the nZVI/rGO composites are suitable as efficient materials for the advanced treatment of Pb(II)-containing wastewater.