Sensitivity of hydrological machine learning prediction accuracy to information quantity and quality

Machine learning (ML) is now commonly employed as a tool for hydrological prediction due to recent advances in computing resources and increases in data volume. The prediction accuracy of ML (or data-driven) modeling is known to be improved through training with additional data; however, the improvement mechanism needs to be better understood and documented. This study explores the connection between the amount of information contained in the data used to train an ML model and the model’s prediction accuracy. The amount of information was quantified using Shannon’s information theory, including marginal and transfer entropy. Three ML models were trained to predict the flow discharge, sediment, total nitrogen, and total phosphorus loads of four watersheds. The amount of information contained in the training data was increased by sequentially adding weather data and the simulation outputs of uncalibrated and/or calibrated mechanistic (or theory-driven) models. The reliability of training data was considered a surrogate of information quality, and accuracy statistics were used to measure the quality (or reliability) of the uncalibrated and calibrated theory-driven modeling outputs to be provided as training data for ML modeling. The results demonstrated that the prediction accuracy of hydrological ML modeling depends on the quality and quantity of information contained in the training data. The use of all types of training data provided the best hydrological ML prediction accuracy. ML models trained only with weather data and calibrated theory-driven modeling outputs could most efficiently improve accuracy in terms of information use. This study thus illustrates how a theory-driven approach can help improve the accuracy of data-driven modeling by providing quality information about a system of interest

Multiple work zone strategy for repetitive on-site work of modular construction using parallel station method

Modular construction offers benefits such as high quality, low cost, and short durations owing to the high productivity of repetitive production. To maximize productivity, modular construction involves repetitive schedules; however, the scheduling methods exhibit limitations when applied to on-site work. These methods are optimized by adjusting the production rate of activities; however, the bounds of the production rates of modular construction on-site work are limited because of workspace limitations in the units and varying amounts of work between activities. This results in idling time in the scheduling methods. Thus, in this research, the parallel station method (PSM) was employed to ensure a flexible production rate. A discrete event simulation model was developed and employed to estimate the number of workers and work duration. The results demonstrated the following: 1) The developed scheduling method exhibits better results than the method for stick-built construction. 2) When applying the PSM, the line-of-balance method is cost-effective, while the TACT method is time-effective, implying that scheduling methods should be selected based on the primary objectives of modular projects. The findings of this research will contribute toward improving the accuracy and applicability of repetitive scheduling methods and reduce the labor cost and duration of on-site work

A fast and scalable qubit-mapping method for noisy intermediate-scale quantum computers

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Evaluation of random forest and regression tree methods for estimation of mass first flush ratio in urban catchments

The identification of factors affecting the first flush phenomenon is important for the control of urban nonpoint source pollution. This study developed machine learning algorithms, the regression tree (RT) and random forest (RF) algorithms, to predict the mass first flush ratio (MFFn) from seven rainfall-related variables. We also evaluated the prediction performance of the two algorithms and the relative importance of the seven variables in model prediction. The RF algorithm outperformed the RT algorithm in terms of Akaike information criterion (AIC) values. In general, the target variables simulated using the RF algorithm had lower AIC values than did those of the RT algorithm, except for T-N and T-P. The RF algorithm also provided acceptable performance statistics for the MFF10 to MFF20 of biochemical oxygen demand (BOD) (R2???=???0.71 and 0.67; Nash-Sutcliffe efficiency (NSE)???=???0.67 and 0.52), chemical oxygen demand (COD) (R2???=???0.70 and 0.71; NSE???=???0.66 and 0.62), total organic carbon (TOC) (R2???=???0.70 and 0.63; NSE???=???0.56 and 0.57), and total phosphorus (T-P) (R2???=???0.72 and 0.71; NSE???=???0.68 and 0.58). Suspended solids (SS) predicted by the RF algorithm showed an acceptable value for R2 (0.68 and 0.64) but a low NSE for the MFF20 (NSE???=???0.48). However, the prediction of MFF30 was unacceptable for all target variables. The rainfall-related variables showed different relative importance estimates among the water quality parameters in the MFFn prediction. Results also showed that BOD and COD were closely associated with rainfall intensity (RI). TOC and T-P showed strong relationships with RI and antecedent rainfall (AR). SS was closely related to RI and rainfall duration (Rdur). T-N showed strong relationships with Rdur, respectively. This study demonstrated that the RF algorithm could be a useful tool to predict the MFF10 and MFF20 of BOD, COD, TOC, and T-P on the basis of rainfall characteristics in urban catchments

Lithium Salt Catalyzed Ring-Opening Polymerized Solid-State Electrolyte with Comparable Ionic Conductivity and Better Interface Compatibility for Li-Ion Batteries

Rechargeable lithium-ion batteries have drawn extensive attention owing to increasing demands in applications from portable electronic devices to energy storage systems. In situ polymerization is considered one of the most promising approaches for enabling interfacial issues and improving compatibility between electrolytes and electrodes in batteries. Herein, we observed in situ thermally induced electrolytes based on an oxetane group with LiFSI as an initiator, and investigated structural characteristics, physicochemical properties, contacting interface, and electrochemical performances of as-prepared SPEs with a variety of technologies, such as FTIR, 1H-NMR, FE-SEM, EIS, LSV, and chronoamperometry. The as-prepared SPEs exhibited good thermal stability (stable up to 210 °C), lower activation energy, and high ionic conductivity (>0.1 mS/cm) at 30 °C. Specifically, SPE-2.5 displayed a comparable ionic conductivity (1.3 mS/cm at 80 °C), better interfacial compatibility, and a high Li-ion transference number. The SPE-2.5 electrolyte had comparable coulombic efficiency with a half-cell configuration at 0.1 C for 50 cycles. Obtained results could provide the possibility of high ionic conductivity and good compatibility through in situ polymerization for the development of Li-ion batteries

Assessing the Potential of Agricultural Reservoirs as the Source of Environmental Flow

Excessive nutrient loadings from drainage areas and resulting water quality degradation in rivers are the major environmental issues around the world. The water quality further deteriorates for the large seasonal variation of precipitation and water flow. Environmental decision makers have been exploring affordable and effective ways of securing environmental flow (EF) to improve the water quality, especially in dry seasons, and agricultural reservoirs have attracted the attention of policymakers as an alternative source of EF. This study proposed an analysis framework for assessing the EF supply potential of agricultural reservoirs as alternative sources of EF. A reservoir water balance model was prepared to mathematically represent the reservoir water balance and quantify temporal variations of the amount of water available for the EF supply. The simulation model was designed to explicitly consider inflow from the upstream drainage areas, irrigation water requirement, and hydrological processes happening in the reservoirs. The proposed framework was applied to four agricultural reservoirs located in South Korea to evaluate its efficiency. Results showed that the additional storage capacity added by the dam reinforcement enabled the study reservoirs to satisfy both needs, EF and irrigation water supply. The surplus capacity turned out to be enough to satisfy various EF supply scenarios at the annual time scale. However, the current operation plans do not consider the seasonal variations of reservoir hydrology and thus cannot supply EF without violating the original operational goal, irrigation water, especially in dry months. The results demonstrate that it is necessary to consider the temporal variations of EF when developing reservoir operation rules and plans to secure EF. This study also highlights the unconventional roles of agricultural reservoirs as resources for improved environmental quality. The methods presented in this study are expected to be a useful tool for the assessment of agricultural reservoirs’ EF supply potential

The regulatory impact of RNA-binding proteins on microRNA targeting

© 2021, The Author(s).Argonaute is the primary mediator of metazoan miRNA targeting (MT). Among the currently identified >1,500 human RNA-binding proteins (RBPs), there are only a handful of RBPs known to enhance MT and several others reported to suppress MT, leaving the global impact of RBPs on MT elusive. In this study, we have systematically analyzed transcriptome-wide binding sites for 150 human RBPs and evaluated the quantitative effect of individual RBPs on MT efficacy. In contrast to previous studies, we show that most RBPs significantly affect MT and that all of those MT-regulating RBPs function as MT enhancers rather than suppressors, by making the local secondary structure of the target site accessible to Argonaute. Our findings illuminate the unappreciated regulatory impact of human RBPs on MT, and as these RBPs may play key roles in the gene regulatory network governed by metazoan miRNAs, MT should be understood in the context of co-regulating RBPs.11Nsciescopu

Tunable high-temperature itinerant antiferromagnetism in a van der Waals magnet

© 2021, The Author(s).Discovery of two dimensional (2D) magnets, showing intrinsic ferromagnetic (FM) or antiferromagnetic (AFM) orders, has accelerated development of novel 2D spintronics, in which all the key components are made of van der Waals (vdW) materials and their heterostructures. High-performing and energy-efficient spin functionalities have been proposed, often relying on current-driven manipulation and detection of the spin states. In this regard, metallic vdW magnets are expected to have several advantages over the widely-studied insulating counterparts, but have not been much explored due to the lack of suitable materials. Here, we report tunable itinerant ferro- and antiferromagnetism in Co-doped Fe4GeTe2 utilizing the vdW interlayer coupling, extremely sensitive to the material composition. This leads to high TN antiferromagnetism of TN ~ 226 K in a bulk and ~210 K in 8 nm-thick nanoflakes, together with tunable magnetic anisotropy. The resulting spin configurations and orientations are sensitively controlled by doping, magnetic field, and thickness, which are effectively read out by electrical conduction. These findings manifest strong merits of metallic vdW magnets as an active component of vdW spintronic applications.11Nsciescopu