Estimation of 1-km Resolution All-Sky Instantaneous Erythemal UV-B with MODIS Data Based on a Deep Learning Method

Although ultraviolet-B (UV-B) radiation reaching the ground represents a tiny fraction of the total solar radiant energy, it significantly affects human health and global ecosystems. Therefore, erythemal UV-B monitoring has recently attracted significant attention. However, traditional UV-B retrieval methods rely on empirical modeling and handcrafted features, which require expertise and fail to generalize to new environments. Furthermore, most traditional products have low spatial resolution. To address this, we propose a deep learning framework for retrieving all-sky, kilometer-level erythemal UV-B from Moderate Resolution Imaging Spectroradiometer (MODIS) data. We designed a deep neural network with a residual structure to cascade high-level representations from raw MODIS inputs, eliminating handcrafted features. We used an external random forest classifier to perform the final prediction based on refined deep features extracted from the residual network. Compared with basic parameters, extracted deep features more accurately bridge the semantic gap between the raw MODIS inputs, improving retrieval accuracy. We established a dataset from 7 Surface Radiation Budget Network (SURFRAD) stations and 1 from 30 UV-B Monitoring and Research Program (UVMRP) stations with MODIS top-of-atmosphere reflectance, solar and view zenith angle, surface reflectance, altitude, and ozone observations. A partial SURFRAD dataset from 2007–2016 trained the model, achieving an R2 of 0.9887, a mean bias error (MBE) of 0.19 mW/m2, and a root mean square error (RMSE) of 7.42 mW/m2. The model evaluated on 2017 SURFRAD data shows an R2 of 0.9376, an MBE of 1.24 mW/m2, and an RMSE of 17.45 mW/m2, indicating the proposed model accurately generalizes the temporal dimension. We evaluated the model at 30 UVMRP stations with different land cover from those of SURFRAD and found most stations had a relative RMSE of 25% and an MBE within ±5%, demonstrating generalization in the spatial dimension. This study demonstrates the potential of using MODIS data to accurately estimate all-sky erythemal UV-B with the proposed algorithm.

Forecasting Facing Economic Shifts, Climate Change and Evolving Pandemics

By its emissions of greenhouse gases, economic activity is the source of climate change which affects pandemics that in turn can impact badly on economies. Across the three highly interacting disciplines in our title, time-series observations are measured at vastly different data frequencies: very low frequency at 1000-year intervals for paleoclimate, through annual, monthly to intra-daily for current climate; weekly and daily for pandemic data; annual, quarterly and monthly for economic data, and seconds or nano-seconds in finance. Nevertheless, there are important commonalities to economic, climate and pandemic time series. First, time series in all three disciplines are subject to non-stationarities from evolving stochastic trends and sudden distributional shifts, as well as data revisions and changes to data measurement systems. Next, all three have imperfect and incomplete knowledge of their data generating processes from changing human behaviour, so must search for reasonable empirical modeling approximations. Finally, all three need forecasts of likely future outcomes to plan and adapt as events unfold, albeit again over very different horizons. We consider how these features shape the formulation and selection of forecasting models to tackle their common data features yet distinct problems.

Experimental investigation and empirical modeling for corrosion rate prediction of biodegradable zinc based composite considering the effect of constituent materials

Biodegradable zinc (Zn) has shown great potential in the area of biomedical applications. Though, the mechanical properties are decisive for the use of Zn for orthopedic and cardiovascular applications. Consequently, one needs to focus on improving the mechanical properties of Zn for its suitability in biomedical applications. Alloying of essential elements of the human body resulted in enhancement of Zn’s mechanical properties in recent years. The corrosion rate of pure Zn is ideal; however, the addition of other elements has resulted in a loss of its ideal corrosion rate. The inclusion of hydroxyapatite (HA) and iron (Fe) in Zn has also been reported in improving the mechanical properties. Hence, a need is raised for the development of a model which can predict the corrosion rate after adding HA along with Fe in Zn. In this research work, empirical based modeling is proposed to predict the corrosion rate, which incorporates the outcome of addition of Fe and HA in Zn. The Zn based materials were fabricated with the help of microwave sintering for developing the empirical model. The corrosion properties of the materials were assessed through a potentiodynamic polarization test in a simulated body fluid solution. The enhanced corrosion rate was attained with the rise in HA (wt%) and Fe (wt%) in Zn. An empirical correlation was established between the influencing controlling parameters (i.e., corrosion current, equivalent weight, and material density) of corrosion rate. Confirmation experiments were conducted to validate the developed model, and the highest error of 6.12% was obtained between the experimental and predicted values exhibiting the efficaciousness of the proposed model.

Explainable Boosting Machines for Slope Failure Spatial Predictive Modeling

Machine learning (ML) methods, such as artificial neural networks (ANN), k-nearest neighbors (kNN), random forests (RF), support vector machines (SVM), and boosted decision trees (DTs), may offer stronger predictive performance than more traditional, parametric methods, such as linear regression, multiple linear regression, and logistic regression (LR), for specific mapping and modeling tasks. However, this increased performance is often accompanied by increased model complexity and decreased interpretability, resulting in critiques of their “black box” nature, which highlights the need for algorithms that can offer both strong predictive performance and interpretability. This is especially true when the global model and predictions for specific data points need to be explainable in order for the model to be of use. Explainable boosting machines (EBM), an augmentation and refinement of generalize additive models (GAMs), has been proposed as an empirical modeling method that offers both interpretable results and strong predictive performance. The trained model can be graphically summarized as a set of functions relating each predictor variable to the dependent variable along with heat maps representing interactions between selected pairs of predictor variables. In this study, we assess EBMs for predicting the likelihood or probability of slope failure occurrence based on digital terrain characteristics in four separate Major Land Resource Areas (MLRAs) in the state of West Virginia, USA and compare the results to those obtained with LR, kNN, RF, and SVM. EBM provided predictive accuracies comparable to RF and SVM and better than LR and kNN. The generated functions and visualizations for each predictor variable and included interactions between pairs of predictor variables, estimation of variable importance based on average mean absolute scores, and provided scores for each predictor variable for new predictions add interpretability, but additional work is needed to quantify how these outputs may be impacted by variable correlation, inclusion of interaction terms, and large feature spaces. Further exploration of EBM is merited for geohazard mapping and modeling in particular and spatial predictive mapping and modeling in general, especially when the value or use of the resulting predictions would be greatly enhanced by improved interpretability globally and availability of prediction explanations at each cell or aggregating unit within the mapped or modeled extent.

An Empirical Modeling of Gate Voltage-Dependent Behaviors of Amorphous Oxide Semiconductor Thin-Film Transistors including Consideration of Contact Resistance and Disorder Effects at Room Temperature

In this paper, we present an empirical modeling procedure to capture gate bias dependency of amorphous oxide semiconductor (AOS) thin-film transistors (TFTs) while considering contact resistance and disorder effects at room temperature. From the measured transfer characteristics of a pair of TFTs where the channel layer is an amorphous In-Ga-Zn-O (IGZO) AOS, the gate voltage-dependent contact resistance is retrieved with a respective expression derived from the current–voltage relation, which follows a power law as a function of a gate voltage. This additionally allows the accurate extraction of intrinsic channel conductance, in which a disorder effect in the IGZO channel layer is embedded. From the intrinsic channel conductance, the characteristic energy of the band tail states, which represents the degree of channel disorder, can be deduced using the proposed modeling. Finally, the obtained results are also useful for development of an accurate compact TFT model, for which a gate bias-dependent contact resistance and disorder effects are essential.