Enhancing User Loyalty through Network Externality: An Empirical Study on B2B Platform

Loyal users are vital to the future of B2B platform with rapid development and intensive competitions. This study examines how network externality, in terms of direct network externality and indirect network externality, enhances B2B platform users\u27 perceived value, and how such perception of value, in turn, influences their satisfaction and loyalty. First, we develop a conceptual model to describe the formation mechanism of user (seller) loyalty on B2B platform. Second, based on literature home and abroad, we develop a questionnaire. With a well-known B2B platform, we get 1,348 valid samples. At last, using structural equation modeling approach, we get the conceptual model fitted. The empirical results show that: network externality can be used as pre-drivers of perceived value thereby affecting user loyalty, but it has no direct influence on user satisfaction

Learning to Behave Like Clean Speech: Dual-Branch Knowledge Distillation for Noise-Robust Fake Audio Detection

Most research in fake audio detection (FAD) focuses on improving performance on standard noise-free datasets. However, in actual situations, there is usually noise interference, which will cause significant performance degradation in FAD systems. To improve the noise robustness, we propose a dual-branch knowledge distillation fake audio detection (DKDFAD) method. Specifically, a parallel data flow of the clean teacher branch and the noisy student branch is designed, and interactive fusion and response-based teacher-student paradigms are proposed to guide the training of noisy data from the data distribution and decision-making perspectives. In the noise branch, speech enhancement is first introduced for denoising, which reduces the interference of strong noise. The proposed interactive fusion combines denoising features and noise features to reduce the impact of speech distortion and seek consistency with the data distribution of clean branch. The teacher-student paradigm maps the student's decision space to the teacher's decision space, making noisy speech behave as clean. In addition, a joint training method is used to optimize the two branches to achieve global optimality. Experimental results based on multiple datasets show that the proposed method performs well in noisy environments and maintains performance in cross-dataset experiments

Functional conservation and divergence of Miscanthus lutarioriparius GT43 gene family in xylan biosynthesis

Background: Xylan is the most abundant un-cellulosic polysaccharides of plant cell walls. Much progress in xylan biosynthesis has been gained in the model plant species Arabidopsis. Two homologous pairs Irregular Xylem 9 (IRX9)/9L and IRX14/14L from glycosyltransferase (GT) family 43 have been proved to play crucial roles in xylan backbone biosynthesis. However, xylan biosynthesis in grass such as Miscanthus remains poorly understood

ADD 2023: the Second Audio Deepfake Detection Challenge

Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks

Tubeimoside 1 Acts as a Chemotherapeutic Synergist via Stimulating Macropinocytosis

Macropinocytosis is a highly conserved endocytic process which characterizes the engulfment of extracellular fluid and its contents into cells via large, heterogeneous vacuoles known as macropinosomes. Tubeimoside-1 (TBM1) is a low toxic triterpenoid saponin extracted from a traditional Chinese herb Bolbostemma paniculatum (Maxim.). TBM1 stimulates a quick accumulation of numerous phase-lucent cytoplasmic vacuoles in multiple colorectal cancer (CRC) cell lines. These vacuoles can be termed as macropinosomes that efficiently engulf lucifer yellow. These vesicles are not overlaps with endocytic organelle tracers, such as ERTracker, LysoTracker and mitoTracker. These vacuoles induced by TBM1 partially incorporate into lysosomes. Transmission electron microscope indicates membrane ruffling to form lamellipodia. Protrusions collapse onto and then fuse back with the plasma membrane to generate these large endocytic vacuoles. Notably, TBM1 efficiently trafficks dextrans into heterotopic xenografts in vivo, thus provide consolidated evidence that the vacuolization can be mainly defined as macropinocytosis. TBM1 downregulates cell viability and increases PI-positive, but not highlighted Hoechst 33342-positive cells. TBM1 induced cell death can be ascribed as methuosis by hyperstimulation of macropinocytosis which can be compromised by amiloride derivative 5-(Nethyl-N-isopropyl). Light chain 3 II is recruited to these vesicles to stimulate macropinocytosis. The cell death and vacuoles can be significantly neutralized by chloroquine, but can not be the inhibited by 3-methyladenine. TBM1 can coordinate with 5-FU to exert toxicity reducing and efficacy enhancing effects in vivo by increasing the uptake of the latter without hepatic injury. In conclusion, TBM1 effectively induces in vitro and in vivo macropinocytosis which can traffick small molecules into CRC cells. It is an attractive drug transporter and can be harnessed as a chemotherapeutic synergist with translational potential

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Overview of the MOSAiC expedition: Physical oceanography

Arctic Ocean properties and processes are highly relevant to the regional and global coupled climate system, yet still scarcely observed, especially in winter. Team OCEAN conducted a full year of physical oceanography observations as part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), a drift with the Arctic sea ice from October 2019 to September 2020. An international team designed and implemented the program to characterize the Arctic Ocean system in unprecedented detail, from the seafloor to the air-sea ice-ocean interface, from sub-mesoscales to pan-Arctic. The oceanographic measurements were coordinated with the other teams to explore the ocean physics and linkages to the climate and ecosystem. This paper introduces the major components of the physical oceanography program and complements the other team overviews of the MOSAiC observational program. Team OCEAN’s sampling strategy was designed around hydrographic ship-, ice- and autonomous platform-based measurements to improve the understanding of regional circulation and mixing processes. Measurements were carried out both routinely, with a regular schedule, and in response to storms or opening leads. Here we present alongdrift time series of hydrographic properties, allowing insights into the seasonal and regional evolution of the water column from winter in the Laptev Sea to early summer in Fram Strait: freshening of the surface, deepening of the mixed layer, increase in temperature and salinity of the Atlantic Water. We also highlight the presence of Canada Basin deep water intrusions and a surface meltwater layer in leads. MOSAiC most likely was the most comprehensive program ever conducted over the ice-covered Arctic Ocean. While data analysis and interpretation are ongoing, the acquired datasets will support a wide range of physical oceanography and multi-disciplinary research. They will provide a significant foundation for assessing and advancing modeling capabilities in the Arctic Ocean

Snow features on sea ice in the western Arctic Ocean during summer 2016

Arctic sea ice and its snow cover are important components of the cryosphere and the climate system. A series of in situ snow measurements were conducted during the seventh Chinese Arctic expedition in summer 2016 in the western Arctic Ocean. In this study, we made an analysis of snow features on Arctic sea ice based on in situ observations and the satellite-derived parameter of snow grain size from MODIS spectral reflectance data. Results indicate that snow depth on Arctic sea ice varied between 19 and 241 mm, with a mean value of 100 mm. The mean density of the snow was 340.4 kg/m3 during the expedition, which was higher than that reported in previous literature. The measurements revealed that a depth hoar layer was widely developed in the snow, accounting for 30%∼50% of the total snow depth. The equivalent snow grain size was small on the surface and large at the bottom in snow pits. The average relative error between MODIS-derived snow grain size and in situ measured surface snow grain size is 14.6%, indicating that remote sensing is a promising method to obtain large-scale information of snow grain size on Arctic sea ice