Prior studies have attributed and confirmed the importance of hosts' information disclosure on boosting their performance due to the prominent information asymmetry on peer-to-peer rental platforms. This study tak...
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Prior studies have attributed and confirmed the importance of hosts' information disclosure on boosting their performance due to the prominent information asymmetry on peer-to-peer rental platforms. This study takes a step further by analyzing how hosts and neighbors' disclosure of descriptions simultaneously and interactively influences the review volume and performance of Airbnb listings. Based on a panel dataset and fixed effect regression models, the results confirmed the findings of prior studies that self-disclosure of descriptions has a positive effect on the review volume and performance. We also initially find a substitute effect between self -disclosure of descriptions and neighbors' disclosure of public information, with a complementary effect be-tween self-disclosure of descriptions and neighbors' disclosure of private information. As a novel attempt to analyze the effect of neighbors' information disclosure on property performance, this study provides important implications for relevant literature and for hospitality professionals to improve information disclosure strategies.
Anode-free lithium metal batteries (AFLMBs) are promising due to ultrahigh energy density, reduced manufacturing costs, and enhanced safety through active lithium elimination. However, their practical implementation r...
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Anode-free lithium metal batteries (AFLMBs) are promising due to ultrahigh energy density, reduced manufacturing costs, and enhanced safety through active lithium elimination. However, their practical implementation remains challenged by unstable electrode-electrolyte interfaces and the resulting rapid active species depletion. Herein, an ultrathin ion-conducting membrane (ICM) is designed, featuring uniformly distributed rigid benzenesulfonimide anionic groups and flexible lithiophilic groups containing ether oxygen groups. The constrained benzenesulfonimide anions enable exceptional charge separation and reduced spatial resistance, boosting lithium-ion mobility, while the integrated lithophilic network directs lateral lithium deposition through ionic nanochannels. This ICM layer effectively promotes the enrichment of anions at the interface and constructs stable anion-derived solid electrolyte interphases (SEI). Meanwhile, ICM layers with electron-insulating and ion-conducting properties can further prevent side reactions, and suppress dendritic Li growth acting as a natural shield, resulting in seamless lithium deposition. Specifically, the Li||Cu coin cells with ICM achieve 99.82% Coulombic efficiency. The AFLMBs assembled with ICM-coated copper foil (ICM Cu) and NCM811 deliver an energy density of 495 Wh kg-1 with 80.72% capacity retention after 100 cycles. The interphasial chemistry design strategy provides insights into the precise interfacial engineering to realize high-performance, high-safety battery systems and facilitates their development for practical applications.
Although the Transformer model has outperformed traditional sequence-to-sequence model in a variety of natural language processing (NLP) tasks, it still suffers from semantic irrelevance and repetition for abstractive...
Although the Transformer model has outperformed traditional sequence-to-sequence model in a variety of natural language processing (NLP) tasks, it still suffers from semantic irrelevance and repetition for abstractive text summarization. The main reason is that the long text to be summarized is usually composed of multi-sentences and has much redundant information. To tackle this problem, we propose a selective and coverage multi-head attention framework based on the original Transformer. It contains a Convolutional Neural Network (CNN) selective gate, which combines n-gram features with whole semantic representation to obtain core information from the long input sentence. Besides, we use a coverage mechanism in the multi-head attention to keep track of the words which have been summarized. The evaluations on Chinese and English text summarization datasets both demonstrate that the proposed selective and coverage multi-head attention model outperforms the baseline models by 4.6 and 0.3 ROUGE-2 points respectively. And the analysis shows that the proposed model generates the summary with higher quality and less repetition.
In the process of extracting naturally-occurring oceanic gas hydrates, the dissociation of hydrates can causea reduction in soil strength. This reduction has the potential to trigger slope failure and submarine landsl...
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ISBN:
(纸本)9781959025610
In the process of extracting naturally-occurring oceanic gas hydrates, the dissociation of hydrates can causea reduction in soil strength. This reduction has the potential to trigger slope failure and submarine landslides,which present a catastrophic threat to offshore facilities and hydrate production. This research aims to create a robust and accurate machine-learning model that can efficiently predict stability of submarine continental slopes where gas hydrates are widespread. By collecting and analyzing 144 relevant cases, a comprehensive dataset was constructed, incorporating slope basic data, overlying and underlying layer data, and geological parameters of the hydrate layer. After conducting a correlation coefficient analysis between the characteristic parameters of the dataset and the output, the key characteristic parameters were determined. To model the dataset and assess its performance, four machine learning techniques were employed: Random Forest(RF), XGBoost, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Formation physical parameters, geotechnical parameters, and NGH parameters were taken as input parameters, and the stability of NGH slopes was taken as prediction indicator. Evaluation metrics such as the ROC and confusion matrix were employed to comprehensively evaluate these models’ classification ability. Among these algorithms,the RF algorithm achieves the best prediction accuracy and AUC value, demonstrating its potential in submarine continental slopes stability prediction of natural gas hydrates. Additionally, sensitive analysis using Gini impurity calculations revealed that hydrate decomposition degree is the most significant factor affecting slope stability, followed by the burial depth and thickness of the hydrate layer. The slope angle,cohesion, and internal friction angle also have significant impacts. This study provides a new perspective for predicting submarine continental slopes stability with NGH and offers a scientific
Apart from the blood-brain barrier (BBB), the efficacy of immunotherapy for glioblastoma (GBM) is limited by the presence of intrinsic and adaptive immune resistance, implying that co-delivery of various immunotherape...
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Apart from the blood-brain barrier (BBB), the efficacy of immunotherapy for glioblastoma (GBM) is limited by the presence of intrinsic and adaptive immune resistance, implying that co-delivery of various immunotherapeutic agents or simultaneous regulation of different cells is urgently needed. Bacterial outer membrane vesicles (OMVs) offer a unique advantage in the treatment of GBM, owing to their multifunctional properties as carriers and immune adjuvants and their ability to cross the BBB. However, traditional OMVs can lead to toxic side effects and disruption of tight junctions in the BBB. Therefore, to enhance the in vivo safety and targeting capability of OMVs, we introduced engineered OMVs to reduce toxicity and further constructed a modularly assembled nanoplatform by performing simple peptide modifications. This nanoplatform demonstrates satisfactory biosafety and is able to continuously cross the BBB and target GBM with the assistance of Angiopep-2. Subsequently, immunogenic substances on OMVs, along with carried small-interfering RNA (siRNA) and doxorubicin, can promote and enhance the reprogramming and phagocytic abilities of macrophages and microglia, respectively, and increase the immunogenicity of GBM, ultimately overcoming GBM immune resistance to enhance the efficacy of immunotherapy. This OMVs-based nanoplatform provides a new paradigm and insights into the development of immunotherapy for GBM.
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