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This study carried out a scenario experiment in Guangxi, China, and used a questionnaire to obtain the research data of the subjects to explore the service recovery effect of local online travel agencies (OTAs) from the perspective of consumer psychology. The empirical results show that consumer confusion will significantly worsen negative word-of-mouth, and negative word-of-mouth will reduce their repurchase intention. In the above empirical impact relationship research, it shows that negative word-of-mouth and consumer forgiveness play a mediating effect and a moderating effect respectively. Because this study is conducive to responding to and solving the new scenario and problems in the practice of OTA services. It will help to improve the risk resistance of OTA enterprises by providing effective service recovery strategies for these enterprises., Author(s): Jiahua Wei [sup.1] , Minkun Liu [sup.2] , Wei Li [sup.1] , Zhiping Hou [sup.1] , Lei Li [sup.1] Author Affiliations: (1) https://ror.org/03z391397, grid.440725.0, 0000 0000 9050 0527, Business [...]

Based on the online shopping service recovery scenario, this study discusses the impact of online store employees' emotional intelligence and psychological empowerment on customers' repurchase intention. This study obtains the research data through the method of scenario experiment. SPSS25.0 and AMOS25.0 were used to test the reliability and validity of the questionnaire data of situational experiment. The direct influence relationship and regulatory effect were analyzed by multi-level regression analysis, and the Bootstrap method was used to test the mediating effect. Through empirical analysis, this study obtains the following results: The emotional intelligence and psychological empowerment of online store employees have a positive impact on the service recovery quality, the service recovery quality has a positive impact on customers' repurchase intention, and the emotional intelligence and psychological empowerment of online store employees have an indirect impact on customers' repurchase intention through the mediating effect t of service recovery quality, Online store reputation plays a positive moderating effect in the relationship between service recovery quality and customers' repurchase intention. This study expands the applicable scenarios of emotional intelligence and psychological empowerment, will provide strategic reference for online stores to carry out effective service recovery, and promote the healthy development of online shopping industry., Author(s): Jiahua Wei [sup.1] , Sai Zhu [sup.1] , Zhiping Hou [sup.1] , Hualong Dong [sup.1] , Jun Li [sup.1] Author Affiliations: (1) grid.440725.0, 0000 0000 9050 0527, Business School, [...]

The service operation modes under the network environment are undergoing some profound changes, and the service recovery effect of online platform ecosystem needs to be paid more attention. Based on the paired questionnaire survey, this paper discusses the impact mechanism of service recovery effect in the platform ecosystem from the perspective of emotional intelligence and relationship quality. The results show that the emotional intelligence of service employees has a positive impact on relationship quality between customers and service providers, and relationship quality between customers and online platforms. The relationship quality between customers and service providers has a positive impact on relationship quality between customers and online platforms, the relationship quality between customers and online platforms has a positive impact on the customer loyalty of online platform. This paper is helpful to deepen the understanding of the impact mechanism of the service recovery effect of the online platform ecosystem, improve the service recovery effect and promote the sound development of the online platforms' business model., Author(s): Jiahua Wei [sup.1] , Xi Lin [sup.1] , Yuguo Jiang [sup.2] , Xiaorui Zhou [sup.1] Author Affiliations: (1) grid.440725.0, 0000 0000 9050 0527, Business School, Guilin University of Technology, [...]

Drought indices do not always provide the most relevant information for water resources management as most of them neglect the role of snow in the land surface water balance. In this study, a physically based drought index, the Standardized Moisture Anomaly Index (SZI), was modified and improved by incorporating the effects of snow dynamics for drought characterization at multiple time scales. The new version of the SZI, called SZIsnow, includes snow in both the water supply and demand in drought characterization by using the water-energy budgets from the Global Land Data Assimilation Systems product. We compared and evaluated the performance of SZIsnow and SZI in drought identification globally across various time scales using observed multicategory drought evidences from several sources. Results show that the SZIsnow agrees better with the observed changes in hydrological and agricultural droughts than the SZI, particularly over basins with high snow accumulation. Furthermore, the SZIsnow is more consistent with the residual water-energy ratio than the SZI over snow-influenced regions. Overall, the SZIsnow can be either a complement or an improvement over the SZI for identifying, monitoring, and characterizing hydrological and agricultural droughts at various scales (e.g., 1-48 months) over high-latitude and high-elevation regions that receive snow.

Lake level changes are critical indicators of hydrological balance and climate change, yet long‐term monthly lake level reconstruction is challenging with incomplete or short‐term data. Data‐driven models, while promising, struggle with nonstationary lake level changes and complex dependencies on meteorological factors, limiting their applicability. Here, we introduce the Hydroformer, a frequency domain enhanced multi‐attention Transformer model designed for monthly lake level reconstruction, utilizing reanalysis data. This model features two innovative mechanisms: (a) Frequency‐Enhanced Attention (FEA) for capturing long‐term temporal dependence, and (b) Causality‐based Cross‐dimensional Attention (CCA) to elucidate how specific meteorological factors influence lake level. Seasonal and trend patterns of catchment meteorological factors and lake level are initially identified by a time series decomposition block, then independently learned and refined within the model. Tested across 50 lakes globally, the Hydroformer excelled in reconstruction periods ranging from half to three times the training‐test length. The model exhibited good performance even when training data missing rates were below 50%, particularly in lakes with significant seasonal fluctuations. The Hydroformer demonstrated robust generalization across lakes of varying sizes, from 10.11 to 18,135 km2, with median values for R2, MAE, MSE, and RMSE at 0.813, 0.313, 0.215, and 0.4, respectively. Furthermore, the Hydroformer outperformed data‐driven models, improving MSE by 29.2% and MAE by 24.4% compared to the next best model, the FEDformer. Our method proposes a novel approach for reconstructing long‐term water level changes and managing lake resources under climate change. Plain Language Summary: Lake water levels, as key indicators of hydrologic dynamics and catchment balance, are vital for understanding climate impacts and managing water resources. However, the lack of continuous measurements for most global lakes, combined with the inability of traditional data‐driven models to effectively decipher complex interactions with catchment hydrological processes, leads to significant gaps in generalizability, accuracy, and reconstructive length. Given these limitations, accurate monthly reconstructions of lake level remain a persistent challenge. To address this, we develop Hydroformer, an innovative frequency domain enhanced multi‐attention Transformer model, utilizing reanalysis data for monthly lake level reconstruction. It employs two innovative attention mechanisms: Frequency‐Enhanced Attention for capturing long‐term temporal dependencies and Causality‐based Cross‐dimensional Attention for cross‐dimensional causal dependencies between catchment meteorological factors and lake level. Through a decomposition block, the model efficiently recognizes and refines inherent seasonal and trend patterns, leading to a comprehensive understanding of lake behaviors. Through testing on 50 global lakes, the Hydroformer has exhibited exceptional performance in reconstructing water levels for lakes ranging from 10.11 to 18,135 km2, adeptly handling short‐term, long‐term, and varying proportions of data gaps. It notably outperforms supervised data‐driven models. This positions it as a vital instrument for monthly lake level reconstruction, showcasing the power of integrating advanced artificial intelligence techniques in hydrological modeling. Key Points: A novel frequency domain enhanced multi‐attention Transformer model, Hydroformer, has been built for reconstructing monthly lake level using reanalysis dataThe model accurately extends reconstructions 2–3 times the training data length, excelling with less than 50% missing training dataHydroformer surpasses advanced AI‐based models, improving MSE and MAE by over 20% and demonstrating strong generalization across lakes of varying sizes [ABSTRACT FROM AUTHOR]

The integration of terrestrial- and satellite-based quantum key distribution (QKD) experiments has markedly advanced global-scale quantum networks, showcasing the growing maturity of quantum technologies. Notably, the use of unmanned aerial vehicles (UAVs) as relay nodes has emerged as a promising method to overcome the inherent limitations of fiber-based and low-Earth orbit (LEO) satellite connections. This paper introduces a protocol for measurement-device-independent QKD (MDI-QKD) using photon orbital angular momentum (OAM) encoding, with UAVs as relay platforms. Leveraging UAV mobility, the protocol establishes a secure and efficient link, mitigating threats from untrusted UAVs. Photon OAM encoding addresses reference frame alignment issues exacerbated by UAV jitter. A comprehensive analysis of atmospheric turbulence, state-dependent diffraction (SDD), weather visibility, and pointing errors on free-space OAM-state transmission systems was conducted. This analysis elucidates the relationship between the key generation rate and propagation distance for the proposed protocol. Results indicate that considering SDD significantly decreases the key rate, halving previous data results. Furthermore, the study identifies a maximum channel loss capacity of 26 dB for the UAV relay platform. This result is pivotal in setting realistic parameters for the deployment of UAV-based quantum communications and lays the foundation for practical implementation strategies in the field. [ABSTRACT FROM AUTHOR]