Due to the difficulties in estimating groundwater recharge and cross‐boundary nature of many aquifers, estimating groundwater recharge at large scale has been called upon. Process‐based models as ...well as data‐driven models have been established to meet this need. Meanwhile, with the advent of explainable artificial intelligence (XAI) methods, data‐driven machine learning models can take advantage of enhanced explainability while keeping the strength of high flexibility. In this study, an ensemble neural network model was built to check the suitability of the model to predict groundwater recharge and the possibility to gain new insights from large data set. Recent large inputs of groundwater recharge data and additional input for the Arabian Peninsula collated in this study were fed to the model with multiple predictors related to climatology considering seasonality, soil and plant characteristics, topography, and hydrogeology. The model showed higher performance (adjusted R2: 0.702, RMSE: 193.35 mm yr−1) than a recent global process‐based model in predicting groundwater recharge. Using XAI methods as individual conditional expectations and Shapley Additive Explanation interaction values, the model behavior was analyzed and possible linear and non‐linear relationships between the predictors and the groundwater recharge rate were found. Long‐term averaged precipitation and enhanced vegetation index showed non‐linear relationships with groundwater recharge rate, while slope, compound topographic index, and water table depth showed low importance to the model results. Most model behaviors followed the domain knowledge, while multi‐correlation between predictors and data skewness hindered the model from learning.
Plain Language Summary
Estimating groundwater recharge rates at a large scale has been an important task among hydrologists. Both process‐based models and data‐driven models have been used for this purpose. Despite their high flexibility and high performance, there has been criticism over data‐driven models, especially machine‐learning models, that the result of the models are difficult to explain. However, new analysis tools called explainable artificial intelligence (XAI) can help explain the model results. In this study, a machine‐learning model (ensemble neural network model) has been built at global scale to check if the model can estimate groundwater recharge rates and to check if the model's behavior explained by XAI can give new insights into the processes. Our model shows higher performance compared to a recent global process‐based model. XAI tools are used to explain how the model predicted the groundwater recharge rates. Long‐term averaged precipitation and enhanced vegetation index show high sensitivity and high importance in predicting groundwater recharge rates, while topographical factors related to slope, curvature, and depth to the groundwater aquifer show low sensitivity and importance.
Key Points
Estimating groundwater recharge rates at global scale using an ensemble neural network model with 5541 observations and 20 predictors
XAI can quantify the sensitivity and importance of each predictor, showing non‐linearities with long‐term precipitation and vegetation index
Predictions show higher accuracy than the current process‐based model, with most behaviors measured by XAI aligning with domain knowledge
The aim of this study was to investigate the impact of menopausal symptoms and menopausal symptom severity on health-related quality of life (HRQoL), work impairment, healthcare utilization, and ...costs.
Data from the 2005 United States National Health and Wellness Survey were used, with only women 40-64 years without a history of cancer included in the analyses (N=8,811). Women who reported experiencing menopausal symptoms (n=4,116) were compared with women not experiencing menopausal symptoms (n=4,695) on HRQoL, work impairment, and healthcare utilization using regression modeling (and controlling for demographics and health characteristic differences). Additionally, individual menopausal symptoms were used as predictors of outcomes in a separate set of regression models.
The mean age of women in the analysis was 49.8 years (standard deviation,±5.9). Women experiencing menopausal symptoms reported significantly lower levels of HRQoL and significantly higher work impairment, and healthcare utilization than women without menopausal symptoms. Depression, anxiety, and joint stiffness were symptoms with the strongest associations with health outcomes.
Menopausal symptoms can be a significant humanistic and economic burden on women in middle age.
Quantifying and monitoring terrestrial water storage (TWS) is an essential task for understanding the Earth's hydrosphere cycle, its susceptibility to climate change, and concurrent impacts for ...ecosystems, agriculture, and water management. Changes in TWS manifest as anomalies in the Earth's gravity field, which are routinely observed from space. However, the complex underlying distribution of water masses in rivers, lakes, or groundwater basins remains elusive. We combine machine learning, numerical modeling, and satellite altimetry to build a downscaling neural network that recovers simulated TWS from synthetic space‐borne gravity observations. A novel constrained training is introduced, allowing the neural network to validate its training progress with independent satellite altimetry records. We show that the neural network can accurately derive the TWS in 2019 after being trained over the years 2003 to 2018. Further, we demonstrate that the constrained neural network can outperform the numerical model in validated regions.
Plain Language Summary
Continuous monitoring of the distribution and movement of continental water masses is essential for understanding the Earth's global water cycle, its susceptibility to climate change, and for risk assessments of ecosystems, agriculture, and water management. Changes of continental water masses are encoded as coarse blob‐like patterns in satellite observations of the Earth's gravity field. Focusing on the South American continent, we introduce a self‐validating artificial neural network to recover detailed and accurate spatiotemporal information of continental water masses from such gravity field observations.
Key Points
South American terrestrial water storage (TWS) is derived from satellite gravity observations with deep learning
A neural network accurately predicts multiscale monthly TWS anomalies in 2019 based on training data from 2003 to 2018
A data assimilation‐like training is introduced, allowing the neural network to validate itself with independent altimetry records
Anxiety disorders have been linked to an increased risk of incident coronary heart disease in which inflammation plays a key pathogenic role. To date, no studies have looked at the association ...between proinflammatory markers and agoraphobia.
In a random Swiss population sample of 2890 persons (35-67 years, 53% women), we diagnosed a total of 124 individuals (4.3%) with agoraphobia using a validated semi-structured psychiatric interview. We also assessed socioeconomic status, traditional cardiovascular risk factors (i.e., body mass index, hypertension, blood glucose levels, total cholesterol/high-density lipoprotein-cholesterol ratio), and health behaviors (i.e., smoking, alcohol consumption, and physical activity), and other major psychiatric diseases (other anxiety disorders, major depressive disorder, drug dependence) which were treated as covariates in linear regression models. Circulating levels of inflammatory markers, statistically controlled for the baseline demographic and health-related measures, were determined at a mean follow-up of 5.5 ± 0.4 years (range 4.7 - 8.5).
Individuals with agoraphobia had significantly higher follow-up levels of C-reactive protein (p = 0.007) and tumor-necrosis-factor-α (p = 0.042) as well as lower levels of the cardioprotective marker adiponectin (p = 0.032) than their non-agoraphobic counterparts. Follow-up levels of interleukin (IL)-1β and IL-6 did not significantly differ between the two groups.
Our results suggest an increase in chronic low-grade inflammation in agoraphobia over time. Such a mechanism might link agoraphobia with an increased risk of atherosclerosis and coronary heart disease, and needs to be tested in longitudinal studies.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The current study characterizes health-related quality of life, work productivity, and resource use among postmenopausal women by severity of vasomotor symptoms (VMS).
Participants were selected from ...the 2010 US National Health and Wellness Survey. Women aged 40 to 75 years who did not report a history of menstrual bleeding or spotting for 1 year were eligible for analysis (N = 3,267). Cohorts of women with no VMS (n = 1,740), mild VMS (n = 931), moderate VMS (n = 462), and severe VMS (n = 134) were compared after controlling for demographic and health characteristics. Outcome measures were assessed using linear models and included health status, work productivity within the past 7 days, and healthcare resource use within the past 6 months.
The mean age of women experiencing severe VMS was 57.92 years. After demographic and health characteristics had been controlled for, women experiencing severe and moderate VMS reported significantly lower mean health status scores compared with women with no symptoms (P < 0.0001). The mean number of menopause symptom-related physician visits was significantly greater among women with severe, moderate, or mild symptoms than among women with no symptoms (P < 0.0001). Among employed women experiencing VMS, women with severe and moderate symptoms had adjusted presenteeism of 24.28% and 14.3%, versus 4.33% in women with mild symptoms (P < 0.001), and activities of daily living impairment of 31.66% and 17.06%, versus 6.16% in women with mild symptoms (P < 0.0001).
In postmenopausal women, a greater severity of VMS is significantly associated with lower levels of health status and work productivity, and greater healthcare resource use.
Background
There has been limited research addressing the effects of constipation on work productivity and healthcare resource use.
Aims
To assess the effect of chronic constipation on health ...outcomes and healthcare resource use.
Methods
Using data from the 2007 National Health and Wellness Survey (NHWS), chronic constipation patients (
n
= 1,430) were propensity score-matched to controls (
n
= 1,430) on demographic and clinical characteristics. Differences between groups in health-related quality of life (SF-12v2), work productivity and activity impairment, and resource use in the last 6 months were examined. Mediation analyses were conducted in order to determine whether the relationship between constipation and resource use was caused by a reduction in health status.
Results
Chronic constipation patients reported significantly lower levels of health-related quality of life (physical component summary score: 39.57 vs. 43.73; mental component summary score: 43.19 vs. 47.86, all
P
-values < 0.01) and significantly higher levels of loss of work productivity and activity impairment (absenteeism: 9.08% vs. 5.20%; presenteeism: 29.52% vs. 19.09%; overall work impairment: 33.65% vs. 21.56%; activity impairment: 46.58% vs. 33.90%, all
P
-values < 0.01) compared to the matched controls. Chronic constipation patients also reported significantly more provider (7.73 vs. 5.63) and emergency room visits (0.52 vs. 0.30) in the past 6 months (all
P
-values < 0.01). Mediation analyses suggested that increased resource use among chronic constipation patients were partially a result of reduced health status.
Conclusions
Compared to matched controls, chronic constipation patients reported greater economic and humanistic burden. Alleviating the humanistic burden associated with constipation may have economic benefits.
The design, energetic performance, and thermal impact of large-scale geothermal collector systems (LSCs) are dependent on the thermal conductivity of unsaturated soils (λ). The aim of this study was ...to investigate the benefits of two different λ measurement methods using single-needle sensor measuring devices on a laboratory scale. Since large-scale determinations are required in the context of LSCs, the potential for deriving λ from electrical resistivity tomography measurements (ERTs) was also examined. Using two approaches—the continuous evaporation method and the punctual method—thermal conductivities of soil samples from Bad Nauheim (Germany) were measured. The results were compared with averaged λ derived from three ERT sections. With the evaporation method, significant bulk density changes were observed during the experimental procedure, which were caused by the clay content and the use of repacked samples. The punctual method ensures a sufficiently constant bulk density during the measurements, but only provides a small number of measurement points. The thermal conductivities derived from ERTs show largely minor deviations from the laboratory measurements on average. If further research confirms the results of this study, ERTs could provide a non-invasive and unelaborate thermal exploration of the subsurface in the context of large-scale infrastructure projects such as LSCs.
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