A Visualization Investigation on the Influence of the Operating Conditions on the Phase Change in the Primary Convergent-divergent Nozzle of a Transcritical CO2 Ejector

Complex flow processes exist in the primary convergent-divergent nozzle of a transcritical CO2 ejector because of the rapid expansion of the supercritical CO2 flow, which have a significant influence on the performance of a transcritical CO2 ejector expansion refrigeration system. A visualization experiment with the direct photography method was carried out to investigate the phase change phenomena in the primary convergent-divergent nozzle of a transcritical CO2 ejector. The visualization transcritical CO2 ejector was designed as a rectangular cross section to minimize the optical distortion. In order to better interpret the phase change phenomena of CO2 flow, four pressure measurement points were lumped in the convergent-divergent nozzle to get the pressure distribution along the convergent-divergent nozzle for various operating conditions. The results revealed that the phase change position in the convergent-divergent nozzle was closely related to the primary flow inlet conditions and the suction flow inlet pressure. .The results showed that the phase change could start after or before the nozzle throat, and the phase change position moved upstream by decreasing the primary flow inlet pressure and temperature simultaneously. As keeping the primary flow inlet pressure constant, the phase change position also moved upward by decreasing the suction flow inlet pressure. In addition, the measured pressure results indicated that the pressure differences in the convergent section of the primary convergent-divergent nozzle increased as the CO2 suction flow inlet pressure decreased because of more adequate expansion of the primary flow

CARNet:Compression Artifact Reduction for Point Cloud Attribute

A learning-based adaptive loop filter is developed for the Geometry-based Point Cloud Compression (G-PCC) standard to reduce attribute compression artifacts. The proposed method first generates multiple Most-Probable Sample Offsets (MPSOs) as potential compression distortion approximations, and then linearly weights them for artifact mitigation. As such, we drive the filtered reconstruction as close to the uncompressed PCA as possible. To this end, we devise a Compression Artifact Reduction Network (CARNet) which consists of two consecutive processing phases: MPSOs derivation and MPSOs combination. The MPSOs derivation uses a two-stream network to model local neighborhood variations from direct spatial embedding and frequency-dependent embedding, where sparse convolutions are utilized to best aggregate information from sparsely and irregularly distributed points. The MPSOs combination is guided by the least square error metric to derive weighting coefficients on the fly to further capture content dynamics of input PCAs. The CARNet is implemented as an in-loop filtering tool of the GPCC, where those linear weighting coefficients are encapsulated into the bitstream with negligible bit rate overhead. Experimental results demonstrate significant improvement over the latest GPCC both subjectively and objectively.Comment: 13pages, 8figure

How Chinese Exchange Students Adapt to Their Academic Course Learningin a US University: A Fresh Look at College English Teaching in China

This paper aims to depict the linguistic challenges that Sino-US exchange students face when they adapt to the demands of English-medium higher education in the US and learning strategies that they came up with to overcome the obstacles in their pursuit of academic learning via in-depth interviews and questionnaire. These findings are complemented by data collected from the real chats, classroom observations, and field notes of over 100 exchange students in a US university. The evidence shows that these students have been tided over the linguistic problems by a combination of learning strategies, strong motivation, diligence, collaborative efforts and resort to reference in Chinese for academic assistance. To probe into the transition period from mainly Chinese-medium courses to those conducted solely in English medium that they have experienced, this article reveals a “thick description” of how thirty exchange Chinese students adapt themselves to English-medium courses by tracking, describing and probing into influences exerted by the exchange program with the aim to revaluate the current College English curriculum prevailing in most universities or colleges in China

Research on Water Pollution Control Based on STM32 Intelligent Vehicle

In order to solve the high cost and low efficiency of different degrees of pollution control of natural water resources in China at this stage, photocatalytic water purification technology is adopted to reduce the cost of water pollution treatment and improve the treatment efficiency, and an intelligent vehicle equipped with photocatalytic materials is proposed, which is equipped with industrial cameras, communication positioning modules and sensors, and realizes dynamic planning of navigation routes by improving ant colony algorithms, computer vision recognition, ultrasonic obstacle avoidance, and realizes photocatalytic fixed-point purification. Predict advanced photoelectric catalytic performance based on density functional theory and machine learning, solve the problem of BiVO4 photo corrosion and instability, and achieve efficient water purification at low cost

Inconsistent Matters: A Knowledge-guided Dual-consistency Network for Multi-modal Rumor Detection

Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers. Though quite a few rumor detection models have exploited the multi-modal data, they seldom consider the inconsistent semantics between images and texts, and rarely spot the inconsistency among the post contents and background knowledge. In addition, they commonly assume the completeness of multiple modalities and thus are incapable of handling handle missing modalities in real-life scenarios. Motivated by the intuition that rumors in social media are more likely to have inconsistent semantics, a novel Knowledge-guided Dual-consistency Network is proposed to detect rumors with multimedia contents. It uses two consistency detection subnetworks to capture the inconsistency at the cross-modal level and the content-knowledge level simultaneously. It also enables robust multi-modal representation learning under different missing visual modality conditions, using a special token to discriminate between posts with visual modality and posts without visual modality. Extensive experiments on three public real-world multimedia datasets demonstrate that our framework can outperform the state-of-the-art baselines under both complete and incomplete modality conditions. Our codes are available at https://github.com/MengzSun/KDCN