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Potassium ion batteries (PIBs) are attractive for the rapidly emerging large-scale energy storage market for intermittent renewable resources. The cost of PIBs can be substantially reduced by utilizing easily synthesized and long cycle-life active materials. This study introduces a one-step solid-state synthesis approach for the encapsulation of FeSe2 nanorods within a ketjenblack (KB) carbon matrix, yielding an FeSe2@C-3 composite. As an anode material for PIBs, it exhibits an excellent cycling performance (a high specific capacity of 286 mA h g−1 after 3500 cycles at 1.0 A g−1). Equally noteworthy is its superior rate performance, demonstrating a reversible specific capacity of 303 mA h g−1 at a high-rate density of 2.0 A g−1. Theoretical calculation confirms that the superior potassium storage performance of FeSe2 is attributed to the low K+ intercalation energy. Additionally, the immobilization of FeSe2 nanorods within the conductive KB network can preserve the electrical and structural integration of the whole electrode. Furthermore, when coupled with perylene-3,4,9,10-tetracarboxylic dianhydride as the cathode, the FeSe2@C-3 full cell sustains a specific capacity of 107 mA h g−1 at 0.1 A g−1 and effectively powers 40 light-emitting diode light bulbs after 200 cycles. This study presents a cost-effective way to produce Fe-based anode materials and introduces a novel structural design strategy aimed at extending the cycle life. [ABSTRACT FROM AUTHOR]

Accurate and realistic geological modelling is the core of oil and gas development and production. In recent years, process-based methods are developed to produce highly realistic geological models by simulating the physical processes that reproduce the sedimentary events and develop the geometry. However, the complex dynamic processes are extremely expensive to simulate, making process-based models difficult to be conditioned to field data. In this work, we propose a comprehensive generative adversarial network framework as a machine-learning-assisted approach for mimicking the outputs of process-based geological models with fast generation. The main objective of our work is to obtain a continuous parametrization of the highly realistic process-based geological models which enables us to calibrate the models and condition the models to data. Numerical results are presented to illustrate the capability of our proposed methodology. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to assess the potential application of a radiomics features‐based nomogram for predicting therapeutic responses to neoadjuvant chemohormonal therapy (NCHT) in patients with high‐risk non‐metastatic prostate cancer (PCa). Methods: Clinicopathologic information was retrospectively collected from 162 patients with high‐risk non‐metastatic PCa receiving NCHT and radical prostatectomy at our center. The postoperative pathological findings were used as the gold standard for evaluating the efficacy of NCHT. The least absolute shrinkage and selection operator (LASSO) was conducted to develop radiomics signature. Multivariate logistic regression analyses were conducted to identify the predictors of a positive pathological response to NCHT, and a nomogram was constructed based on these predictors. Results: Sixty‐three patients (38.89%) experienced positive pathological response to NCHT. Receiver operating characteristic analyses showed that the area under the curve (AUC) of periprostatic fat (PPF) radiomics signature was 0.835 (95% CI, 0.754–0.898), while the AUC of intratumoral radiomics signature was 0.822 (95% CI, 0.739–0.888). Multivariate logistic regression analysis revealed that PSA level, PPF radiomics signature and intratumoral radiomics signature were independent predictors of positive pathological response. A nomogram based on these three predictors was constructed. The AUC was 0.908 (95% CI, 0.839–0.954). The Hosmer–Lemeshow goodness‐of‐fit test showed that the nomogram was well calibrated. Decision curve analysis revealed the favorable clinical practicability of the nomogram. The nomogram was successfully validated in the validation cohort. Kaplan–Meier analyses showed that nomogram and positive pathological response were significantly related with survival of PCa. Conclusion: The radiomics–clinical nomogram based on mpMRI radiomics features exhibited superior predictive ability for positive pathological response to NCHT in high‐risk non‐metastatic PCa. [ABSTRACT FROM AUTHOR]

For chloride-contaminated RC structures, steel corrosion is one of the most common reasons causing the deterioration of reinforced concrete (RC) structures. Electrochemical chloride extraction (ECE), which migrates the chloride ions out of the RC structure, is preferred as it is a nondestructive repairing method. In this context, electrokinetic models are often used to investigate ECE processes. Currently, electrokinetic models based on different potential theories, such as the constant potential, electroneutrality, and Gauss’ law, may be used to solve the potential distribution. Although many ECE numerical simulations have been conducted, there has been no systematic comparison of models based on different potential theories. Therefore, in this study, a comprehensive comparative study has been conducted on these models in terms of the potential distribution, current density, quantity of electric charge, and ion concentration. Two commonly used ECE methods, namely, steady voltage and steady current, were numerically simulated and analyzed. The numerical results with actual ECE data were also compared. Although the electrokinetic model based on electroneutrality might overestimate the residual chloride concentration and underestimate current density and hydroxyl concentration, critical analysis suggests that electroneutrality is more appropriate than the constant potential and Gauss’ law for electrokinetic modeling.