Developing a dual entropy-transinformation criterion for hydrometric network optimization based on information theory and copulas.

Hydrometric information collected by monitoring networks is fundamental for effective management of water resources. In recent years, entropy-based multi-objective criterions have been developed for the evaluation and optimization of hydrometric networks, and copula functions have been frequently used in hydrological frequency analysis to model multivariate dependence structures. This study developed a dual entropy-transinformation criterion (DETC) to identify and prioritize significant stations and generate candidate network optimization solutions. The criterion integrated an entropy index computed with mathematical floor function and a transinformation index computed with copula entropy through a tradeoff weight. The best fitted copula models were selected from three Archimedean copula families, i.e., Gumbel, Frank and Clayton. DETC was applied to a streamflow monitoring network in the Fenhe River basin and two rainfall monitoring networks in the Beijing Municipality and the Taihu Lake basin, which covers different network classification, network scale, and climate type. DETC was assessed by the commonly used dual entropy-multiobjective optimization (DEMO) criterion and was compared with a minimum transinformation (MinT) based criterion for network optimization. Results showed that DETC could effectively prioritize stations according to their significance and incorporate decision preference on information content and information redundancy. Comparison of the isohyet maps of two rainstorm events between DETC and MinT showed that DETC had advantage of restoring the spatial distribution of precipitation.

Improved comprehensive ecological risk assessment method and sensitivity analysis of polycyclic aromatic hydrocarbons (PAHs).

Based on the existing comprehensive ecological risk assessment methods of PAHs, this paper proposed an improved hierarchical Archimedean copula integral assessment (HACIA) model with the optimization in the model selection mechanism and accelerating the calculation speed, and according to which performed the sensitivity analysis of the integrated risk relative to the underlying grouped risk probability. Taihu Lake in China and the Bay of Santander in Spain were taken as study areas, whose samples were obtained and extracted concentrations of 16 priority polycyclic aromatic hydrocarbons (PAHs). After briefly analyzing their concentration characteristics and source, their comprehensive ecological risks were evaluated by the improve HACIA model and their sensitivity was also analyzed. The results proved that, for Taihu Lake, pyrogenic sources occupied the dominance, especially grass, coal and wood combustion, while the risk proportion of 5-rings PAHs was the lowest indeed based on the improved HAICA model. For the Bay of Santander, source apportionment indicated both petrogenic and pyrogenic sources, mainly from vehicle emissions including gasoline and diesel engines, and 4-ring PAHs were urgently needed to be managed. However, the sensitivity analysis results of two study areas showed that the most effective control target for reducing integral risk has no obvious relationship with the maximum grouped risk. And a clear linear relationship between the maximum sensitivity range and the logarithm of the initial overall risk only presented in one of study areas, which required further research to clarify. In brief, the improved HACIA model is helpful to evaluate the comprehensive ecological risk of 16 PAHs, and formulate risk management strategies based on grouped risk assessment and sensitivity analysis, with the former points out the admonitory risk and the latter helps to find the most effective mitigation measures.

Evaluation of information transfer and data transfer models of rain-gauge network design based on information entropy.

Rainfall is one of the most fundamental components of the water cycle and is one of the fundamental inputs of hydrological models. A well-designed network can not only depict the regional precipitation characteristics, but also economically yield maximum needed rainfall information. In regions where either there is limited data or data is not available, it is a common challenge to add stations. The entropy theory-based information transfer model and geostatistical interpolation techniques are two solutions to meet the challenge. In this study, we used a representative rain gauge network to do the network design. Two models, based on information transfer and data transfer, were compared for network design. Other rain gauges in the study area were used as reference ("true values") for assessing the model. Results showed that the information transfer model estimated transinformation between station pairs better than did the data transfer model. Different representative gauges were evaluated separately by the directional information transfer index (DIT). The candidate gauges selected with least information redundancy were similar for both information transfer and data transfer models. Though both models captured some least information-redundant areas, other areas may be bypassed because of model errors or estimation errors.

A hybrid wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series.

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.

A kriging and entropy-based approach to raingauge network design.

Hydrological data, such as precipitation, is fundamental for planning, designing, developing, and managing water resource projects as well as for hydrologic research. An optimal raingauge network leads to more accurate estimates of mean or point precipitation at any site over the watershed. Some studies in the past have suggested increasing gauge network density for reducing the estimation error. However, more stations mean more cost of installation and monitoring. This study proposes an approach on the basis of kriging and entropy theory to determine an optimal network design in the city of Shanghai, China. Unlike the past studies using kriging interpolation and entropy theory for network design, the approach developed in the current study not only used the kriging method as an interpolator to determine rainfall data at ungauged locations but also incorporated the minimum kriging standard error (KSE) and maximum net information (NI) content. The approach would thus lead to an optimal network and would enable the reduction of kriging standard error of precipitation estimates throughout the watershed and achieve an optimum rainfall information. This study also proposed an NI-KSE-based criterion which is dependent on a single-objective optimization. To evaluate the final optimal gauge network, areal average rainfall was estimated and its accuracy was compared with that obtained with the existing rain gauge network.

Generational differences in the relationship between media exposure and health behaviors during COVID-19 pandemic.

Based on a questionnaire survey (N = 857), this study analyzed generational differences in the public health behaviors of COVID-19 and provided an explanation for generational differences from the perspective of media exposure. There are significant differences in media exposure and health behaviors between the Mesozoic generation (35-55) and the young generation (18-34) during the lull. The Mesozoic generation paid greater attention to information on pandemics. Consequently, their health behaviors surpass that of the young generation. On the basis of social cognitive theory and protection motivation theory, this study develops a mediating model of media exposure on health behaviors, demonstrating that media exposure can influence health behaviors through the mediating effects of perceived severity, self-efficacy, and response efficacy, but not via perceived susceptibility. Moreover, a moderated mediation study found that generation moderates the indirect effect of media exposure on health behaviors via perceived susceptibility. Media exposure influences Mesozoic healthy behaviors positively by decreasing their perceived susceptibility. The implication of this study is that the development of health communication theory must account for generational differences and disease-specific characteristics.

A study on digital inclusion of Chinese rural older adults from a life course perspective.

Digital inclusion is viewed as a crucial strategy for promoting social inclusion and addressing issues related to aging. This study focuses on the digital inclusion practices of rural senior citizens and introduces a life course research perspective to move the study of influencing factors from the proximal to the distal end. The motivations, trajectories, and barriers to digital inclusion among rural elderly groups are presented through participant observation and semi-structured interviews with 34 elderly people in a village in northern China. It was discovered that digital inclusion or exclusion is the cumulative result of life events, social roles, and personal agency in the life course, while it is difficult for the elderly to break away from traditional culture and social relations in rural areas in the digital age. The digital practice is not only an inner adjustment in the process of life stage transition for rural seniors but also an individual pursuit of active aging.

A Machine Learning-Based Predictive Model of Epidermal Growth Factor Mutations in Lung Adenocarcinomas.

Data from 758 patients with lung adenocarcinoma were retrospectively collected. All patients had undergone computed tomography imaging and EGFR gene testing. Radiomic features were extracted using the medical imaging tool 3D-Slicer and were combined with the clinical features to build a machine learning prediction model. The high-dimensional feature set was screened for optimal feature subsets using principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO). Model prediction of EGFR mutation status in the validation group was evaluated using multiple classifiers. We showed that six clinical features and 622 radiomic features were initially collected. Thirty-one radiomic features with non-zero correlation coefficients were obtained by LASSO regression, and 24 features correlated with label values were obtained by PCA. The shared radiomic features determined by these two methods were selected and combined with the clinical features of the respective patient to form a subset of features related to EGFR mutations. The full dataset was partitioned into training and test sets at a ratio of 7:3 using 10-fold cross-validation. The area under the curve (AUC) of the four classifiers with cross-validations was: (1) K-nearest neighbor (AUCmean = 0.83, Acc = 81%); (2) random forest (AUCmean = 0.91, Acc = 83%); (3) LGBM (AUCmean = 0.94, Acc = 88%); and (4) support vector machine (AUCmean = 0.79, Acc = 83%). In summary, the subset of radiographic and clinical features selected by feature engineering effectively predicted the EGFR mutation status of this NSCLC patient cohort.

Multi-tissue metabolomic profiling reveals potential mechanisms of cocoon yield in silkworms (Bombyx mori) fed formula feed versus mulberry leaves.

Use of formula feed (FF) for silkworms for all instars, has promoted transformation and progress in traditional sericulture. However, the cocoon yield of FF silkworms has failed to reach that of silkworms fed mulberry leaves (ML). The biological mechanisms underlying this phenomenon have not been well described. This study aimed to identify metabolic mechanisms and potential biomarkers relating to the poor cocoon yield of FF silkworms. In this study, silkworms received treatments of either ML (ML group) or FF (FF group) for all instars. At the 3rd day of the 5th instar, the midgut (MG), hemolymph (HL) and posterior silk gland (PSG) were collected for the metabolome profiles detection. The remaining silkworms were fed ML or FF until cocooning for investigation. The whole cocoon yield (WCY) was significantly higher in the FF group than the ML group (p < 0.05), whereas the cocoon shell weight (CSW) and cocoon shell rate (CSR) were significantly lower in the FF group (p < 0.05). A total of 845, 867 and 831 metabolites were qualified and quantified in the MG, HL and PSG of the FF silkworms, respectively. Correspondingly, 789, 833 and 730 metabolites were quantified in above three tissues of the ML group. Further, 230, 249 and 304 significantly different metabolites (SDMs) were identified in the MG, HL and PSG between the FF and ML group, respectively. Eleven metabolic pathways enriched by the SDMs were mutual among the three tissues. Among them, cysteine and methionine metabolism, arginine biosynthesis, and arginine and proline metabolism were the top three pathways with the highest impact value in the PSG. Six biomarkers were obtained through biomarker analysis and Pearson correlation calculation. Among them, homocitrulline, glycitein, valyl-threonine, propyl gallate and 3-amino-2,3-dihydrobenzoic acid were positively correlated with WCY, but negatively correlated with CSW and CSR (p < 0.05). An opposite correlation pattern was observed between 3-dimethylallyl-4-hydroxyphenylpyruvate and the three cocoon performance traits. Overall, three key metabolic pathways and six biomarkers associated with cocoon yield were interpreted, and should provide directions for formula feed optimization in factory-raised silkworms.

Combined analysis of silk synthesis and hemolymph amino acid metabolism reveal key roles for glycine in increasing silkworm silk yields.

Rearing silkworms (Bombyx mori) using formula feed has revolutionized traditional mulberry feed strategies. However, low silk production efficiencies persist and have caused bottlenecks, hindering the industrial application of formula feed sericulture. Here, we investigated the effects of formula feed amino acid composition on silk yields. We showed that imbalanced amino acids reduced DNA proliferation, decreased Fib-H, Fib-L, and P25 gene expression, and caused mild autophagy in the posterior silk gland, reducing cocoon shell weight and ratio. When compared with mulberry leaves, Gly, Ala, Ser, and Tyr percentages of total amino acids in formula feed were decreased by 5.26%, while Glu and Arg percentages increased by 9.56%. These changes increased uric acid and several amino acids levels in the hemolymph of silkworms on formula feed. Further analyses showed that Gly and Thr (important synthetic Gly sources) increased silk yields, with Gly increasing amino acid conversion efficiencies to silk protein, and reducing urea levels in hemolymph. Also, Gly promoted endomitotic DNA synthesis in silk gland cells via phosphoinositide 3-kinase (PI3K)/Akt/target of rapamycin (TOR) signaling. In this study, we highlighted the important role of Gly in regulating silk yields in silkworms.

Stochastic sensitivity analysis of nitrogen pollution to climate change in a river basin with complex pollution sources.

It is increasingly recognized that climate change could impose both direct and indirect impacts on the quality of the water environment. Previous studies have mostly concentrated on evaluating the impacts of climate change on non-point source pollution in agricultural watersheds. Few studies have assessed the impacts of climate change on the water quality of river basins with complex point and non-point pollution sources. In view of the gap, this paper aims to establish a framework for stochastic assessment of the sensitivity of water quality to future climate change in a river basin with complex pollution sources. A sub-daily soil and water assessment tool (SWAT) model was developed to simulate the discharge, transport, and transformation of nitrogen from multiple point and non-point pollution sources in the upper Huai River basin of China. A weather generator was used to produce 50 years of synthetic daily weather data series for all 25 combinations of precipitation (changes by - 10, 0, 10, 20, and 30%) and temperature change (increases by 0, 1, 2, 3, and 4 °C) scenarios. The generated daily rainfall series was disaggregated into the hourly scale and then used to drive the sub-daily SWAT model to simulate the nitrogen cycle under different climate change scenarios. Our results in the study region have indicated that (1) both total nitrogen (TN) loads and concentrations are insensitive to temperature change; (2) TN loads are highly sensitive to precipitation change, while TN concentrations are moderately sensitive; (3) the impacts of climate change on TN concentrations are more spatiotemporally variable than its impacts on TN loads; and (4) wide distributions of TN loads and TN concentrations under individual climate change scenario illustrate the important role of climatic variability in affecting water quality conditions. In summary, the large variability in SWAT simulation results within and between each climate change scenario highlights the uncertainty of the impacts of climate change and the need to incorporate extreme conditions in managing water environment and developing climate change adaptation and mitigation strategies.