Search

Predicting drought severity is essential for drought management and early warning systems. Although numerous physical model-based and data-driven methods have been put forward for drought prediction, their abilities are largely constrained by data requirements and modeling complexity. There remains a challenging task to efficiently predict categorial drought, especially for the U.S. Drought Monitor (USDM). Aiming at this issue, multiple Markov chains for USDM-based categorial drought prediction are successfully proposed and evaluated in this paper. In particular, this study concentrated on how the Markov order, step size, and training set length affected prediction accuracy (PA). According to experiments from 2000 to 2021, it was found that the 1-step and first-order Markov models had the best accuracy in predicting droughts up to 4 weeks ahead. The PA steadily dropped with increasing step size, and the average accuracy at monthly scale was 88%. In terms of seasonal variability, summer (July-August) had the lowest PA while winter had the highest (January-February). In comparison with the western region, the PA in the eastern United States is 25% higher. Moreover, the length of the training set had an obvious impact on the PA of the model. The PA in 1-step prediction was 87% and 78% under 20- and 5-yr training sets, respectively. The results of the study showed that Markov models predicted categorical drought with high accuracy in the short term and showed different performances on time and space scales. Proper use of Markov models would help disaster managers and policy makers to put mitigation policies and measures into practice. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) have attracted intensive attention due to the efficient event-driven computing paradigm. Among SNN training methods, the ANN-to-SNN conversion is usually regarded to achieve state-of-the-art recognition accuracies. However, many existing ANN-to-SNN techniques impose lengthy post-conversion steps like threshold balancing and weight renormalization, to compensate for the inherent behavioral discrepancy between artificial and spiking neurons. In addition, they require a long temporal window to encode and process as many spikes as possible to better approximate the real-valued ANN neurons, leading to a high inference latency. To overcome these challenges, we propose a calcium-gated bipolar leaky integrate and fire (Ca-LIF) spiking neuron model to better approximate the functions of the ReLU neurons widely adopted in ANNs. We also propose a quantization-aware training (QAT)-based framework leveraging an off-the-shelf QAT toolkit for easy ANN-to-SNN conversion, which directly exports the learned ANN weights to SNNs requiring no post-conversion processing. We benchmarked our method on typical deep network structures with varying time-step lengths from 8 to 128. Compared to other research, our converted SNNs reported competitively high-accuracy performance, while enjoying relatively short inference time steps. [ABSTRACT FROM AUTHOR]

In the process of OLED device coating, alignment accuracy is an important factor to determine the resolution and yield of OLED panel. In the process of OLED device, after value is used to indicate the alignment deviation of substrate and mask before and after coating. Therefore, after value is commonly used to indicate the reliability of alignment accuracy in the industry. Among the factors that affect the after value, the magnet's attraction to the block is the most important, and the deflection of magnets with different support structures is very different, which directly leads to the influence of different magnet support structures on the after value. Therefore, this paper analyzes the magnets with different support structures, and verifes them with experiments, and finally obtains the influence trend relationship of magnet support structures on the after value. In addition, the deflection of the magnet also affects the size of the shadow area to a great extent. The influence of the magnet on the after value and shadow is analyzed below. [ABSTRACT FROM AUTHOR]

Water scarcity and water quality are becoming increasingly serious global issues, especially in China. Collecting accurate, scientific data about water-scarce areas is a critical first step for water resources management. Beijing is one of the cities in China that faces severe water scarcity issues, impeding economic and social development. This study assesses the water deficit in Beijing at the catchment level using the Water Evaluation and Planning (WEAP) model to provide insights for water assignment and to explore the advantages of WEAP applications in water resources management. Results show that (1) Beijing had a water shortage of 560.24 million cubic meters in 2010 with agricultural water facing the most severe shortage; (2) Catchment Yongdinghe faces the most severe water shortage challenge with demand site reliability of 45.83%, followed by Daqinghe, Jiyunhe, and Beiyunhe (64.17%–67.92%), and Chaobaihe (80.83%); and (3) the most sensitive water scarcity months are November, December, and February, characterized by the mean water demand coverage of 44.37% in all catchment areas. This study provides insights for water allocation and future research by serving as an important basis for water balance and for sustaining economic–social–environmental development in China. [ABSTRACT FROM AUTHOR]

We experimentally and theoretically demonstrate a tunable bandpass filter based on the optically induced transparency enabled by dynamical interference of continuous radiation modes and localized whispering gallery modes (WGMs) with a surface nanoscale axial photonics (SNAP) platform. The tunability in bandwidth and power transfer efficiency is achieved by scanning the microfiber which achieves a high power transfer efficiency up to 1.43, high out-of-band rejection of ∼31 dB, and bandwidth tunability of 30.6 MHz ∼ 2.32 GHz. The combination of easy alignment, fiber compatibility, high efficiency, and robust wide-range tunability facilitates applications ranging from quantum optics to photonic networks. [ABSTRACT FROM AUTHOR]