This study investigates dynamic relationships among US corn cash prices for the years 2006–2011. Using daily data from 182 markets in seven Midwestern states, an error correction model is estimated ...and directed acyclic graphs characterize the contemporaneous causal relationships among prices from different states. Empirical results based on the PC algorithm show that three states, Iowa, Ohio, and Minnesota, dominate corn cash prices. Four potential causal paths among the three states also are identified. Given that physical flows of grain are different at different times of the year, the data are divided into storage periods and harvest periods, and a VAR in differences is adopted to model the price relationships. While Iowa and Ohio continue to dominate corn prices during the storage period, the causal flows are mixed during the harvest period. An application of the LiNGAM algorithm refines the results relative to those derived from the PC algorithm and reveals that Iowa, the leading corn-producing state, is the only state that dominates pricing over the crop year.
Titanium dioxide (TiO2) photocatalysts in the form of thin films are of great interest due to their tunable optical band gaps, E g’s, which are promising candidates for applications of visible-light ...photocatalytic activities. Previous studies have shown that processing conditions, dopant types and concentrations, and different combinations of the two have great impacts on structural, microscopic, and optical properties of TiO2 thin films. The lattice parameters and surface area are strongly correlated with E g values, which are conventionally simulated and studied through first-principle models, but these models require significant computational resources, particularly in complex situations involving codoping and various surface areas. In this study, we develop the Gaussian process regression model for predictions of anatase TiO2 photocatalysts’ energy band gaps based on the lattice parameters and surface area. We explore 60 doped-TiO2 anatase photocatalysts with E g’s between 2.280 and 3.250 eV. Our model demonstrates a high correlation coefficient of 99.99% between predicted E g’s and their experimental values and high prediction accuracy as reflected through the prediction root-mean-square error and mean absolute error being 0.0012 and 0.0010% of the average experimental E g, respectively. This modeling method is simple and straightforward and does not require a lot of parameters, which are advantages for applications and computations.
Photodetectors (PDs), as an indispensable component in electronics, are highly desired to be flexible to meet the trend of next‐generation wearable electronics. Unfortunately, no in‐depth reviews on ...the design strategies, material exploration, and potential applications of wearable photodetectors are found in literature to date. Thus, this progress report first summarizes the fundamental design principles of turning “hard” photodetectors “soft,” including 2D (polymer and paper substrate‐based devices) and 1D PDs (fiber shaped devices). In short, the flexibility of PDs is realized through elaborate substrate modification, material selection, and device layout. More importantly, this report presents the current progress and specific requirements for wearable PDs according to the application: monitoring, imaging, and optical communication. Challenges and future research directions in these fields are proposed at the end. The purpose of this progress report is not only to shed light on the basic design principles of wearable PDs, but also serve as the roadmap for future exploration in wearable PDs in various applications, including health monitoring and Internet of Things.
The fundamental design principles of turning “hard” photodetectors “soft” are summarized and the materials selection, mainly from the perspective of substrate choice (polymer, paper, and fiber), are explored. The current progress, requirements, challenges, and future opportunities for wearable photodetectors for different applications: monitoring, imaging, and optical communication are discussed in detail.
In this paper, we present a novel approach for 3D dental model segmentation via deep Convolutional Neural Networks (CNNs). Traditional geometry-based methods tend to receive undesirable results due ...to the complex appearance of human teeth (e.g., missing/rotten teeth, feature-less regions, crowding teeth, extra medical attachments, etc.). Furthermore, labeling of individual tooth is hardly enabled in traditional tooth segmentation methods. To address these issues, we propose to learn a generic and robust segmentation model by exploiting deep Neural Networks, namely NNs. The segmentation task is achieved by labeling each mesh face. We extract a set of geometry features as face feature representations. In the training step, the network is fed with those features, and produces a probability vector, of which each element indicates the probability a face belonging to the corresponding model part. To this end, we extensively experiment with various network structures, and eventually arrive at a 2-level hierarchical CNNs structure for tooth segmentation: one for teeth-gingiva labeling and the other for inter-teeth labeling. Further, we propose a novel boundary-aware tooth simplification method to significantly improve efficiency in the stage of feature extraction. After CNNs prediction, we do graph-based label optimization and further refine the boundary with an improved version of fuzzy clustering. The accuracy of our mesh labeling method exceeds that of the state-of-art geometry-based methods, reaching 99.06 percent measured by area which is directly applicable in orthodontic CAD systems. It is also robust to any possible foreign matters on model surface, e.g., air bubbles, dental accessories, and many more.
Solid-state refrigeration techniques have drawn increasing attention due to their potential for improving the energy efficiency of refrigeration and temperature-control systems without using harmful ...gas as in conventional gas compression techniques. Research on magnetocaloric lanthanum manganites with near-room-temperature Curie temperature shows promising results for development of magnetic refrigeration devices. Chemical substitutions are one of the most effective methods to tune the magnetocaloric effect, represented by the maximum magnetic entropy change (MMEC), through the incorporation of various lanthanides, rare-earth elements, alkali metals, alkaline-earth metals, transition metals, and other elements. Some theories based on lattice distortions and double-exchange interactions show that ionic radii of the dopants and final compositions correlate with the MMEC, but the correlations are generally limited to A-site substitutions and become less applicable to multi-doped manganites than single-doped ones. In this work, the Gaussian process regression model is developed as a machine learning tool to find statistical correlations between the MMEC and structural parameters among lanthanum manganites. More than 70 lattices, cubic, pseudocubic, orthorhombic, and rhombohedral, with the MMEC ranging from 0.65 J kg−1 K−1 to 8.00 J kg−1 K−1 under a field change of 5 T are explored for this purpose. Structural parameters utilized as descriptors include ionic radii at both A- and B-sites, ⟨Mn–O⟩ bond length, ⟨Mn–O–Mn⟩ bond angle, and compositions consisting of up to six elements. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of the magnetocaloric effect.
Ni-based single crystal superalloys exhibit superb mechanical strength, particularly, creep resistance at elevated temperature. The unique microstructure, which is consisted of
γ
and
γ
′
phases, is a ...major factor that determines the mechanical behavior of these alloys. The lattice misfit between the two phases is of particular interest in understanding and predicting the deformation mechanism. The measurement of the lattice misfit by advanced analytical instruments is costly and difficult. In current study, we develop the Gaussian process regression model to predict lattice misfits for Ni-based single crystal superalloys based on chemical composition, temperature, and two morphological indicators. The model is highly stable and accurate and promising as a fast, robust, and low-cost tool for lattice misfit estimations.
Graphic Abstract
As an important thermophysical property, polymers' glass transition temperature, Tg, could sometimes be difficult to determine experimentally. Modeling methods, particularly data-driven approaches, ...are promising alternatives to predictions of Tg in a fast and robust way. The molecular traceless quadrupole moment and molecule average hexadecapole moment are closely correlated with polymers' Tg. In the current work, these two parameters are used as descriptors in the Gaussian process regression model to predict Tg. We investigate 60 samples with Tg values from 194 K to 440 K. The model provides rapid and low-cost Tg estimations with high accuracy and stability.
Materials science; Materials chemistry; Physical chemistry; Glass transition temperature; Polymer; Machine learning; Gaussian process regression
Solar radiation, especially ultraviolet (UV) light, is a major hazard for most skin‐related cancers. The growing needs for wearable health monitoring systems call for a high‐performance real‐time UV ...sensor to prevent skin diseases caused by excess UV exposure. To this end, here a novel self‐powered p‐CuZnS/n‐TiO2 UV photodetector (PD) with high performance is successfully developed (responsivity of 2.54 mA W−1 at 0 V toward 300 nm). Moreover, by effectively replacing the Ti foil with a thin Ti wire for the anodization process, the conventional planar rigid device is artfully turned into a fiber‐shaped flexible and wearable one. The fiber‐shaped device shows an outstanding responsivity of 640 A W−1, external quantum efficiency of 2.3 × 105%, and photocurrent of ≈4 mA at 3 V, exceeding those of most current UV PDs. Its ultrahigh photocurrent enables it to be easily integrated with commercial electronics to function as a real‐time monitor system. Thus, the first real‐time wearable UV radiation sensor that reads out ambient UV power density and transmits data to smart phones via wifi is demonstrated. This work not only presents a promising wearable health monitor, but also provides a general strategy for designing and fabricating smart wearable electronic devices.
A real‐time wearable UV sensor for prevention of skin cancers caused by excess UV radiation exposure is demonstrated. The fiber‐shaped device consisting of a novel p‐CuZnS/n‐TiO2 nanotube array structure exhibits an outstanding photocurrent and external quantum efficiency, a fast response speed, and self‐powered property, which make it a promising wearable real‐time health monitor.
•We develop nonlinear autoregressive neural network (with exogenous inputs) models for daily agricultural commodity price forecasting.•We explore the forecasting issue for soybeans and soybean oil ...over a period of greater than fifty years.•We construct simple, accurate, and stable models for the two agricultural commodities of economic significance.•The models are useful as technical forecasting tools and for policy analysis.
Price forecasting is a key concern for market participants in the agriculture sector. This study explores usefulness of the nonlinear autoregressive neural network (NARNN) and NARNN with exogenous inputs (NARNN–X) for forecasting issues in data sets of daily prices over periods of greater than fifty years for soybeans and soybean oil. The exogenous input in the NARNN–X for prices of soybeans (soybean oil) are prices of soybean oil (soybeans). Through investigating various model settings across the algorithm, delay, hidden neuron, and data splitting ratio, models resulting in accurate and stable performance for these two commodities are arrived at. The overall relative root mean square errors based on the chosen NARNNs (NARNN–Xs) are 1.701% (1.695%) and 1.777% (1.775%) for soybeans and soybean oil, respectively. Usefulness of the machine learning approach for price forecasting issues of the two commodities is demonstrated, as well as potential usefulness of prices of closely related commodities for providing predictive content. Results here could be used on a standalone basis as technical forecasts or combined with fundamental forecasts for forming perspectives of price trends and conducting policy analysis. The empirical framework here should not be difficult to implement, which is an important consideration to many decision makers, and has potential to be generalized for forecasting prices of other commodities.
•We develop neural network models for house price forecasting in China.•We explore the forecasting issue for 100 major Chinese cities.•We construct simple, accurate, and stable models.•The models are ...useful as technical tools and for policy analysis.
The house market has been rapidly growing for the past decade in China, making price forecasting an important issue to the people and policy makers. We approach this problem by exploring neural networks for forecasting of house prices from one hundred major cities for the period of June 2010–May 2019, serving as the first study with such wide coverage for the emerging Chinese market through a machine learning technique. We aim at constructing simple and accurate neural networks as a contribution to pure technical forecasting of house prices. To facilitate the analysis, we investigate different model settings over the algorithm (the Levenberg-Marquardt, scaled conjugate gradient, and Bayesian regularization), delay (from two to six), hidden neuron (two, three, five, and eight), and data spitting ratio (70%–15%–15%, 60%–20%–20%, and 80%–10%–10% for trainingvalidationtesting), and arrive at a rather simple neural network with only four delays and three hidden neurons that leads to stable performance of 1% average relative root mean square error across the one hundred cities for the training, validation, and testing phases. We demonstrate the usefulness of the machine learning approach to the house price forecasting problem in the Chinese market. Our results could be used on a standalone basis or combined with fundamental forecasting in forming perspectives of house price trends and conducting policy analysis. Our empirical framework should not be difficult to deploy, which is an important consideration to many decision makers, and has potential to be generalized for house price forecasting of other cities in China.
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