Query with Assumptions for Probabilistic Relational Databases

Users may have prior knowledge about a probabilistic database. They prefer to query over a probabilistic database on their prior knowledge which cannot be written as component clauses of conventional SQL queries. A naive approach is to query over a new database version, which is generated by transforming the original probabilistic database to satisfy users\u27 prior knowledge; however, it is impractical to generate a different probabilistic database version for each prior knowledge. In this paper, we propose the concept of the query with assumptions which allow users to describe their prior knowledge with a newly introduced ASSUMPTION clause of SQL. We also propose an approach to obtain the result of a query based on assumption clauses. The experimental studies show our approach has better performance compared to the naive approach

A moving least square immersed boundary method for SPH with thin-walled structures

This paper presents a novel method for smoothed particle hydrodynamics (SPH) with thin-walled structures. Inspired by the direct forcing immersed boundary method, this method employs a moving least square method to guarantee the smoothness of velocity near the structure surface. It simplifies thin-walled structure simulations by eliminating the need for multiple layers of boundary particles, and improves computational accuracy and stability in three-dimensional scenarios. Supportive three-dimensional numerical results are provided, including the impulsively started plate and the flow past a cylinder. Results of the impulsively started test demonstrate that the proposed method obtains smooth velocity and pressure in the, as well as a good match to the references results of the vortex wake development. In addition, results of the flow past cylinder test show that the proposed method avoids mutual interference on both side of the boundary, remains stable for three-dimensional simulations while accurately calculating the forces acting on structure.Comment: 15 pages,11 figure

Intervention and Mechanism of Dexmedetomidine on A549 Cells Injured by Hypoxia/Reoxygenation

Objective: To investigate the effect and mechanism of dexmedetomidine (DEX) on hypoxia /reoxygenation (H/R) injury of A549 cells. Methods: A549 cells were cultured and randomly divided into four groups (n=10): Normoxic group;DEX group; H/R injury group;H/R injury+DEX intervention group. Observe the morphological changes of cells; Cell viability was detected by cck-8 assay. TUNEL assay was used to detect apoptosis index (AI).Expressions of GRP78, CHOP, JNK, caspase-12, caspase-3 proteins and mRNA were detected by Western Blot and RT-PCR;Detect the activity of caspase-3. Results: Compared with the H group, the OD value and AI value in the HD group were significantly up-regulated, apoptotic cells were significantly decreased, the expressions of CHOP, caspase-12, p-JNK and caspase-3 proteins and mRNA were significantly decreased, the GRP78 protein and mRNA increased, and the caspase-3 activity was significantly decreased, the differences were statistically significant (P<0.01). Conclusion: Dexmedetomidine has a protective effect on A549 cells after H/R injury, which may be related to its inhibition of apoptosis induced by excessive endoplasmic reticulm stress. Keywords: Dexmedetomidine; Hypoxia/Reoxygenationinjury;Apoptosis;Endoplasmicreticulm stress

Research Progress of Functional Enzymes inTraditional Fermented Food

Traditional fermented foods could produce a variety of functional enzymes and plenty of flavor compounds through microbial fermentation. The production process of fermented foods would be constantly enriched with abundant enzymes, and it is the current hotspot to study these functional enzymes systematically and scientifically. In this study, the traditional isolation and culture of functional enzymes from fermented food are analyzed, and it can identify the enzyme activity of key functional enzymes. Furthermore, this work summarizes the recent research on functional enzymes in fermented foods by high-throughput technologies, such as PICRUSt analysis based on 16S rRNA and ITS sequencing, metagenome, metatranscriptome and metaproteome. It is found that, compared to analysis by traditional isolation and culture, single high-throughput technology can obtain more functional enzymes information, analyze complex metabolic pathways and identify active functional enzymes. The research on functional enzymes by combinations of high-throughput technologies is also reviewed, and it is found that combination of different methods can obtain more information to analyze the fermentation process of traditional food comprehensively and deeply. It is also found that, based on analysis of high-throughput technology, some researchers have analyzed the specific functional enzymes in traditional foods by heterologous expression. This study comprehensively summarizes results and methods of functional enzymes in various traditional fermented foods, aiming to provide technical foundation for the research and application of functional enzymes in traditional fermented foods

Improving Certified Robustness via Statistical Learning with Logical Reasoning

Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently. However, current robustness certification methods are only able to certify under a limited perturbation radius. Given that existing pure data-driven statistical approaches have reached a bottleneck, in this paper, we propose to integrate statistical ML models with knowledge (expressed as logical rules) as a reasoning component using Markov logic networks (MLN, so as to further improve the overall certified robustness. This opens new research questions about certifying the robustness of such a paradigm, especially the reasoning component (e.g., MLN). As the first step towards understanding these questions, we first prove that the computational complexity of certifying the robustness of MLN is #P-hard. Guided by this hardness result, we then derive the first certified robustness bound for MLN by carefully analyzing different model regimes. Finally, we conduct extensive experiments on five datasets including both high-dimensional images and natural language texts, and we show that the certified robustness with knowledge-based logical reasoning indeed significantly outperforms that of the state-of-the-art

Facility or Transport Inequality? Decomposing Healthcare Accessibility Inequality in Shenzhen, China

Accessibility to healthcare services is crucial for residents’ wellbeing. Numerous studies have revealed significant spatial inequality in healthcare accessibility across various contexts. However, it still remains unclear whether the inequality is caused by the unbalanced spatial distribution of healthcare facilities or by unequal transport access to them. This study decomposes inequality in healthcare accessibility into facility- and transport-driven inequality by comparing scenarios of healthcare accessibility, which consider various combinations of multidimensional components of accessibility using different distance measures. Using a case study in Shenzhen, this study reveals that both facility distribution and transport access substantially contribute to spatial inequality in healthcare accessibility. Facility distribution accounts for 61.3% and 50.8% of the overall accessibility inequality for driving and transit modes, respectively. The remaining inequality is induced by imbalanced mobility provided by transport networks. Furthermore, the impact of transport component on healthcare accessibility is unevenly distributed. This study highlights that both facility- and transport-related countermeasures should be considered to improve the accessibility and equality of healthcare services. It provides transferable methods for quantitatively decomposing facility- and transport-driven inequality in accessibility to healthcare or other facilities

Time Delay Optimization of Compressing Shipborne Vision Sensor Video Based on Deep Learning

As the technology for offshore wireless transmission and collaborative innovation in unmanned ships continues to mature, research has been gradually carried out in various countries on methods of compressing and transmitting perceptual video while driving ships remotely. High Efficiency Video Coding (H.265/HEVC) has played an extremely important role in the field of Unmanned Aerial Vehicle (UAV) and autopilot, and as one of the most advanced coding schemes, its performance in compressing visual sensor video is excellent. According to the characteristics of shipborne vision sensor video (SVSV), optimizing the coding aspects with high computational complexity is one of the important methods to improve the video compression performance. Therefore, an efficient video coding technique is proposed to improve the efficiency of SVSV compression. In order to optimize the compression performance of SVSV, an intra-frame coding delay optimization algorithm that works in the intra-frame predictive coding (PC) session by predicting the Coding Unit (CU) division structure in advance is proposed in combination with deep learning methods. The experimental results show that the total compression time of the algorithm is reduced by about 45.49% on average compared with the official testbed HM16.17 for efficient video coding, while the Bjøntegaard Delta Bit Rate (BD-BR) increased by an average of 1.92%, and the Peak Signal-to-Noise Ratio (BD-PSNR) decreased by an average of 0.14 dB