Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep neural network for prediction of diet quality among doctors and nurses in North China during the COVID-19 pandemic

Objective The COVID-19 pandemic has placed unprecedented pressure on front-line healthcare workers, leading to poor health status, especially diet quality. This study aimed to develop a diet quality prediction model and determine the predictive effects of personality traits, socioeconomic status, lifestyles, and individual and working conditions on diet quality among doctors and nurses during the COVID-19 pandemic. Methods A total of 5,013 doctors and nurses from thirty-nine COVID-19 designated hospitals provided valid responses in north China in 2022. Participants’ data related to social-demographic characteristics, lifestyles, sleep quality, personality traits, burnout, work-related conflicts, and diet quality were collected with questionnaires. Deep Neural Network (DNN) was applied to develop a diet quality prediction model among doctors and nurses during the COVID-19 pandemic. Results The mean score of diet quality was 46.14 ± 15.08; specifically, the mean scores for variety, adequacy, moderation, and overall balance were 14.33 ± 3.65, 17.99 ± 5.73, 9.41 ± 7.33, and 4.41 ± 2.98, respectively. The current study developed a DNN model with a 21–30–28-1 network framework for diet quality prediction. The DNN model achieved high prediction efficacy, and values of R2, MAE, MSE, and RMSE were 0.928, 0.048, 0.004, and 0.065, respectively. Among doctors and nurses in north China, the top five predictors in the diet quality prediction model were BMI, poor sleep quality, work–family conflict, negative emotional eating, and nutrition knowledge. Conclusion During the COVID-19 pandemic, poor diet quality is prevalent among doctors and nurses in north China. Machine learning models can provide an automated identification mechanism for the prediction of diet quality. This study suggests that integrated interventions can be a promising approach to improving diet quality among doctors and nurses, particularly weight management, sleep quality improvement, work-family balance, decreased emotional eating, and increased nutrition knowledge.
Controlling steam flood migration using air injection wells

Plumes of contamination emanate from nonaqueous-phase liquid sources as its constituents slowly dissolve into passing groundwater. For a large, well-characterized source, an aggressive technology such as steam flooding can accelerate cleanup. Steam is injected through a series of wells within and around a source area, and the steam zone grows radially around each injection well. The steam front drives the contamination to a system of groundwater pumping wells in the saturated zone and soil vapor extraction wells in the vadose zone. The movement of steam in the subsurface is governed primarily by soil heterogeneity and gravity. Steam is buoyant in groundwater and tends to migrate upward unless injected below a continuous confining layer. Groundwater pumping rates and vacuums typical of steam flooding are generally low compared to the steam injection rate and pressure and have limited influence over the lateral growth of the steam zone. To overcome these limitations, a system of air injection wells can be used to direct the steam zone growth. This paper presents results of sand-box experiments using air injection to prevent the outward growth of a steam zone between extraction wells with a discontinuous confining layer limiting the upward migration of steam. These experiments were numerically modeled with the multiphase nonisothermal code T2VOC. When confined vertically, the experiments and modeling show that outward migration of the steam front can be effectively controlled by placing air injection wells opposite steam injection wells. This technique can direct steam zone growth toward difficult access sources and away from areas where steam is not desired. Control of a proposed full-scale steam flood of the M-Area settling basin at the Savannah River Site was modeled using this method and the results are presented in this paper.
A multi-physics ensemble modeling framework for reliable C2n estimation

Free-Space Optical Communication (FSOC) links are considered a key technology to support the increasing needs of our connected, data-heavy world, but they are prone to disturbance through atmospheric processes such as optical turbulence. Since turbulence is highly dependent on local topographic and meteorological conditions, modeling optical turbulence strength (see manuscript PDF for symbol) is challenging during the design phase of an optical link or network. Over the past 25 years, (see manuscript PDF for symbol) parameterizations of varying complexities have been combined with various numerical weather prediction models for the spatio-temporal estimation of (see manuscript PDF for symbol). However, the outputs of these models can exhibit substantial variability based on the user-defined configuration that determines how atmospheric processes are represented. To address this concern, we propose to run not a single model configuration but multiple diverse ones to generate an ensemble estimate of (see manuscript PDF for symbol). We employ the Weather Research and Forecasting model (WRF) with ten different Planetary Boundary Layer (PBL) physics schemes forming a diverse ensemble yielding a probabilistic (see manuscript PDF for symbol) estimate. We demonstrate that this ensemble outperforms the individual runs when compared to scintillometer field measurements and show it to be robust against outliers. We believe that FSOC downstream tasks such as link budget estimations should also become more robust if based on a (see manuscript PDF for symbol) ensemble estimate compared to single model runs.
Tissue-specific expression of senescence biomarkers in spontaneously hypertensive rats: evidence of premature aging in hypertension

Background Cellular senescence is an age-related physiological process that contributes to tissue dysfunction and accelerated onset of chronic metabolic diseases including hypertension. Indeed, elevation of blood pressure in hypertension coincides with premature vascular aging and dysfunction. In addition, onsets of metabolic disturbance and osteopenia in patients with hypertension have also been reported. It is possible that hypertension enhances premature aging and causes progressive loss of function in multiple organs. However, the landscape of cellular senescence in critical tissues affected by hypertension remains elusive. Materials and Methods Heart, liver, bone, hypothalamus, and kidney were collected from spontaneously hypertensive rats (SHR) and age- and sex-matched normotensive Wistar rats (WT) at 6, 12, 24 and 36 weeks of age (n = 10 animals/group). Changes in mRNA levels of senescence biomarkers namely cyclin-dependent kinase (CDK) inhibitors (CDKIs), i.e., Cdkn2a (encoding p16Ink4a) and Cdkn1a (encoding p21cip1) as well as senescence-associated secretory phenotypes (SASPs), i.e., Timp1, Mmp12, Il6 and Cxcl1, were determined. Additionally, bone collagen alignment and hydroxy apatite crystal dimensions were determined by synchrotron radiation small- and wide-angle X-ray scattering (SAXS/WAXS) techniques. Results Real-time PCR revealed that transcript levels of genes encoding CDKIs and SASPs in the heart and liver were upregulated in SHR from 6 to 36 weeks of age. Expression of Timp1 and Cxcl1 was increased in bone tissues isolated from 36-week-old SHR. In contrast, we found that expression levels of Timp1 and Il6 mRNA were decreased in hypothalamus and kidney of SHR in all age groups. Simultaneous SAXS/WAXS analysis also revealed misalignment of bone collagen fibers in SHR as compared to WT. Conclusion Premature aging was identified in an organ directly affected by high blood pressure (i.e., heart) and those with known functional defects in SHR (i.e., liver and bone). Cellular senescence was not evident in organs with autoregulation of blood pressure (i.e., brain and kidney). Our study suggested that cellular senescence is induced by persistently elevated blood pressure and in part, leading to organ dysfunction. Therefore, interventions that can both lower blood pressure and prevent cellular senescence should provide therapeutic benefits for treatment of cardiovascular and metabolic consequences.
Investigation of the summer 2018 European ozone air pollution episodes using novel satellite data and modelling

Abstract. In the summer of 2018, Europe experienced an intense heatwave which coincided with several persistent large-scale ozone (O3) pollution episodes. Novel satellite data of lower-tropospheric column O3 from the Global Ozone Monitoring Experiment-2 (GOME-2) and Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp satellite showed substantial enhancements in 2018 relative to other years since 2012. Surface observations also showed ozone enhancements across large regions of continental Europe in summer 2018 compared to 2017. Enhancements to surface temperature and the O3 precursor gases carbon monoxide and methanol in 2018 were co-retrieved from MetOp observations by the same scheme. This analysis was supported by the TOMCAT chemistry transport model (CTM) to investigate processes driving the observed O3 enhancements. Through several targeted sensitivity experiments we show that meteorological processes, and emissions to a secondary order, were important for controlling the elevated O3 concentrations at the surface. However, mid-tropospheric (∼ 500 hPa) O3 enhancements were dominated by meteorological processes. We find that contributions from stratospheric O3 intrusions ranged between 15 %–40 %. Analysis of back trajectories indicates that the import of O3-enriched air masses into Europe originated over the North Atlantic, substantially increasing O3 in the 500 hPa layer during summer 2018.
Tunable Electronic Transport of New-Type 2D Iodine Materials Affected by the Doping of Metal Elements

2D iodine structures under high pressures are more attractive and valuable due to their special structures and excellent properties. Here, electronic transport properties of such 2D iodine structures are theoretically studied by considering the influence of the metal-element doping. In equilibrium, metal elements in Group 1 can enhance the conductance dramatically and show a better enhancement effect. Around the Fermi level, the transmission probability exceeds 1 and can be improved by the metal-element doping for all devices. In particular, the device density of states explains well the distinctions between transmission coefficients originating from different doping methods. Contrary to the “big” site doping, the “small” site doping changes transmission eigenstates greatly, with pronounced electronic states around doped atoms. In non-equilibrium, the conductance of all devices is almost weaker than the equilibrium conductance, decreasing at low voltages and fluctuating at high voltages with various amplitudes. Under biases, K-big doping shows the optimal enhancement effect, and Mg-small doping exhibits the most effective attenuation effect on conductance. Contrastingly, the currents of all devices increase with bias linearly. The metal-element doping can boost current at low biases and weaken current at high voltages. These findings contribute much to understanding the effects of defects on electronic properties and provide solid support for the application of new-type 2D iodine materials in controllable electronics and sensors.
Importance of bimaterial interfaces for earthquake dynamics and strong ground motion

SUMMARY We perform a numerical investigation of dynamic ruptures on a bimaterial interface in 3-D with regularized slip-weakening friction and a heterogeneous initial shear stress and discuss the resulting strong ground motion. To isolate effects introduced by the material contrast, we perform pairs of simulations with opposite material orientations. We show that for many parameter sets the dynamics of rupture propagation are significantly influenced by the broken symmetry due to the material contrast during rupture propagation. The resulting slip histories of two events with reversed orientations of the material contrast can deviate such that the emanating waves lead to large differences in peak ground motion (peak ground velocity and peak ground acceleration), even when slip-distribution of the individual events are very similar, and therefore their moment magnitudes are basically identical. We also show that the wrinkle-like slip pulse specific to the bimaterial mechanism can be nucleated naturally from an initially crack-like mode of rupture, when the initial stress allows for large propagation distances. Once such a pulse has been nucleated, it travels at a dominant propagation speed close to the generalized Rayleigh velocity. The dynamic weakening of the fault due to the normal stress alteration during slip allows nucleating ruptures to overcome asperities of low initial shear stress in the preferred direction, which is the direction of displacement on the seismically slower side of the fault. In such situations, the orientation of the material contrast determines rupture extent and therefore the size of the earthquake, potentially by orders of magnitude.
Noncontact in Bed Measurements of Electrocardiogram Using a Capacitively Coupled Electrode Array Based on Flexible Circuit Board

Unconstrained vital sign monitoring technology has been a hot topic of research in recent years as people increasingly focus on their health status and sleep quality. Conventional contact electrocardiogram (ECG) measurement technology has difficulties in meeting the application needs of sleep scenarios due to short validity period and low comfort. To handle this problem, ECG measurement using noncontact capacitively coupled sensing technology, which do not require direct skin contact for ECG signal acquisition, could be an alternative; however, this method requires fixing the ECG measurement electrodes in two specific positions, which makes it difficult to meet the needs for body differences and multiple sleep postures. This study presents a noncontact sleep ECG measurement system based on a flexible electrode array, which can acquire ECG signals through pajamas and bed sheets using the principle of capacitive coupling. The experiment results showed that the average signal-to-noise ratio (SNR) of the data measured in the supine and lateral postures of subjects was above 30 dB, and the respiratory waves (RWs) with the same frequency as contact ECG measurement system could be extracted. We also used this system to acquire ECG signals from multiple points on the dorsal of four subjects and analyzed their variation patterns, and the experimental results were in high agreement with the ECG potential employment recorded by 32-lead body surface maps. Meanwhile, the T-wave, R-wave, S-wave peaks, and the ratio of T and R waves of the collected ECG signals were extracted as features. Based on this, the action classification model was trained using long short-term memory network (LSTM) to classify the postures of four subjects in supine, left lateral and right lateral positions, and the classification accuracies were 95.83%, 98.94%, 98.80%, and 98.30%, respectively. Although our prototype system must be further improvement and development, these results provide a strong basis for future realization of electrode array-based ECG signal selection algorithms. The proposed system has great potential value in detecting abnormalities of ECG, heart rate (HR), respiration and other vital indicators during human sleep and realizing personalized medical treatment.
Finding the Truth From Uncertain Time Series by Differencing

Time series data is ubiquitous and of great importance in real applications. But due to poor qualities and bad working conditions of sensors, time series reported by them contain more or less noises. To reduce noise, multiple sensors are usually deployed to measure an identical time series and from these observations the truth can be estimated, which derives the problem of truth discovery for uncertain time series data. Several algorithms have been proposed, but they mainly focus on minimizing the error between the estimated truth and the observations. In our study, we aim at minimizing the noise in the estimated truth. To solve this optimization problem, we first find out the level of noise produced by each sensor based on differenced time series, which can help estimating the truth wisely. Then, we propose a quadratic optimization model to minimize the noise of the estimated truth. Further, a post process is introduced to refine the result by iteration. Experimental results on both real world and synthetic data sets verify the effectiveness and efficiency of our proposed methods, respectively.
The Driving Forces Analysis of Economic Growth in Henan Province

The method of statistical analysis is used to analyze the data of the classification value-added growth in Henan province from 1998 to 2012. The 11 industries loading coefficients for the provincial economic growth are designed to study the change rules of the economic growth, the temporal relationship of classification value-added growth and overall economic growth, the main driving forces, as well as the potential driving forces in the future. The results show that the main driving forces of economic growth successively come from animal husbandry, forestry, information transmission & software & information technology services and construction industry in the year of 2012, and the potential driving forces successively come from transportation & storage & postal industry, services of farming & forestry & animal husbandry & fishery, wholesale & retail trades & hotels & catering industry and farming in future.
Determinants of liquidity risk: Do corporate governance practices have an impact? An empirical study of emerging markets

Most banks are concerned about how liquidity risk affects their performance. Managing liquidity risk is critical since failing to do so will result in a bank’s insolvency. This study aims to investigate systematic and unsystematic factors that affect the liquidity risk of Egyptian banks during the period 2000–2022. A dynamic panel data and generalized method of moments (GMM) estimator is used. Results revealed that systematic factors have no impact on the liquidity risk of Islamic banks and are more exposed to unsystematic factors. Since corporate governance practices have an impact on banks performance we assume corporate governance practices have an impact on liquidity risk determinants as well. Therefore, applying good corporate governance practices will mitigate the liquidity risk of Egyptian banks. The second part of this study examines which banking system applies corporate governance practices more effectively, and if it has an impact on factors that most affect liquidity risk. Traditional banks and traditional banks with Islamic windows have governance practices more effectively to mitigate the impact of systemic and unsystematic risks on a bank’s liquidity risk. Islamic banks apply governance practices less effectively. This is attributed to the presence of a Sharia Committee as an alternative to applying governance practices.
Improving the quantiﬁcation of climate change hazards by hydrological models: A simple approach for mimicking the impact of active vegetation on potential evapotranspiration

Almost no hydrological model takes into account that changes in evapotranspiration are affected by how the vegetation responds to changing CO 2 and climate. This severely limits their ability to quantify the impact of climate change on evapotranspiration and thus water resources. We developed a simple approach for mimicking, in hydrological models, the impact of active vegetation on potential evapotranspiration (PET) under climate change. This approach can be applied for climate 5 change impact studies by hydrological models that compute PET as a function of net radiation and temperature only, i.e., using the Priestley-Taylor (PT) equation. Our approach is based on the work of Milly and Dunne (2016) (MD), which compared the change of non-water-stressed actual evapotranspiration (NWSAET) as computed by an ensemble of global climate models (GCM) with various methods for computing PET change. MD proposed to estimate the impact of climate change on PET as a function of only the change in net energy input at the land surface, i.e., to neglect changes in other climate variables.The new 10 mimicking approach (PT-MA) for application in hydrological models retains the impact of temperature on daily to interannual as well as spatial PET variations but removes the impact of the long-term temperature trend on PET such that long-term changes in future PET are driven by changes in net radiation only. We implemented the PT-MA approach in the global hydrological model WaterGAP 2.2d and computed daily time series of PET between 1901 and 2099 using the bias-adjusted output of four GCMs for RCP8.5. With PT-MA, increases of GCM-derived NWSAET between the end of the 20th and the end of the 21st 15 century are simulated well by WaterGAP, while severely overestimated with the standard PT. Application of the mimicking approach in WaterGAP results in smaller future decreases or larger future increases in renewable water resources (RWR) as compared to neglecting active vegetation, except in a small number of grid cells where increased inﬂow from upstream due to increased upstream runoff leads to enhanced evapotranspiration from surface water bodies or irrigated ﬁelds. On about 20% of the global land area, the mimicking approach leads to an increase of RWR that is more than 20% higher than when neglecting 20 the active vegetation, while on more than 10% of the global land area, projected RWR decrease is lowered by more than 20%. We recommend applying the mimicking approach to assess climate change hazards by any hydrological model that does not include stomatal conductance when calculating PET. PET type determine both mimicking approaches to similar PET changes for any
Landscape-Level Estimation of Nitrogen Removal in Coastal Louisiana Wetlands: Potential Sinks under Different Restoration Scenarios

ABSTRACT Rivera-Monroy, V.H. ; Branoff, B. ; Meselhe, E. ; McCorquodale, A. ; Dortch, M. ; Steyer, G.D. ; Visser, J., and Wang, H. , 2013. Landscape-level estimation of nitrogen removal in coastal Louisiana wetlands: potential sinks under different restoration scenarios. Coastal eutrophication in the northern Gulf of Mexico (GOM) is the primary anthropogenic contributor to the largest zone of hypoxic bottom waters in North America. Although biologically mediated processes such as denitrification (Dn) are known to act as sinks for inorganic nitrogen, it is unknown what contribution denitrification makes to landscape-scale nitrogen budgets along the coast. As the State of Louisiana plans the implementation of a 2012 Coastal Master Plan (MP) to help restore its wetlands and protect its coast, it is critical to understand what effect potential restoration projects may have in altering nutrient budgets. As part of the MP, a spatial statistical approach was developed to estimate nitrogen removal under varying scenarios of future conditions and coastal restoration project implementation. In every scenario of future conditions under which MP implementation was modeled, more nitrogen () was removed from coastal waters when compared with conditions under which no action is taken. Overall, the MP increased coast-wide average nitrogen removal capacity (NRC) rates by up to 0.55 g N m−2 y−1 compared with the “future without action” (FWOA) scenario, resulting in a conservative estimate of up to 25% removal of the annual + load of the Mississippi-Atchafalaya rivers (956,480 t y−1). These results are spatially correlated, with the lower Mississippi River and Chenier Plain exhibiting the greatest change in NRC. Since the implementation of the MP can maintain, and in some regions increase the NRC, our results show the need to preserve the functionality of wetland habitats and use this ecosystem service (i.e. Dn) to decrease eutrophication of the GOM.