Concentrated Geo-Privacy

This paper proposes concentrated geo-privacy (CGP), a privacy notion that can be considered as the counterpart of concentrated differential privacy (CDP) for geometric data. Compared with the previous notion of geo-privacy [ABCP13, CABP13], which is the counterpart of standard differential privacy, CGP offers many benefits including simplicity of the mechanism, lower noise scale in high dimensions, and better composability known as advanced composition. The last one is the most important, as it allows us to design complex mechanisms using smaller building blocks while achieving better utilities. To complement this result, we show that the previous notion of geo-privacy inherently does not admit advanced composition even using its approximate version. Next, we study three problems on private geometric data: the identity query, k nearest neighbors, and convex hulls. While the first problem has been previously studied, we give the first mechanisms for the latter two under geo-privacy. For all three problems, composability is essential in obtaining good utility guarantees on the privatized query answer

A Multi-Service Composition Model for Tasks in Cloud Manufacturing Based on VS-ABC Algorithm

This study analyzes the impact of Industry 4.0 and SARS-CoV-2 on the manufacturing industry, in which manufacturing entities are faced with insufficient resources and uncertain services; however, the current study does not fit this situation well. A multi-service composition for complex manufacturing tasks in a cloud manufacturing environment is proposed to improve the utilization of manufacturing service resources. Combining execution time, cost, energy consumption, service reliability and availability, a quality of service (QoS) model is constructed as the evaluation standard. A hybrid search algorithm (VS–ABC algorithm) based on the vortex search algorithm (VS) and the artificial bee colony algorithm (ABC) is introduced and combines the advantages of the two algorithms in search range and calculation speed. We take the customization production of automobiles as an example, and the case study shows that the VS–ABC algorithm has better applicability compared with traditional vortex search and artificial bee colony algorithms

An experimental study of imbibition process and fluid distribution in tight oil reservoir under different pressures and temperatures

Tight reservoirs are a major focus of unconventional reservoir development. As a means to improve hydrocarbon recovery from tight reservoirs, imbibition has been received increasing attentions in recent years. This study evaluates how the changes in temperature and pressure affect imbibition through conducting experimental tests under various conditions on samples from the Yan Chang formation, a tight reservoir in Ordos Basin. The fluid distribution is compared before and after imbibition in core samples by nuclear magnetic resonance method. The results show that the imbibition recovery is significantly improved through increasing temperature and pressure. A high temperature facilitates molecular thermal movements, increasing oil-water exchange rate. The core samples are characterized with nano-mesopores, which is followed by nano-macropores, micropores, mesopores, and nano-micropores. Comparative analysis of nuclear magnetic resonance shows that the irreducible water saturation increases after imbibition and is mainly distributed in nano-pores. Increasing pressure increases the amount of residual water in nano pores, with the relatively more significant increase in the amount of residual water in nanomacro-pores compared with other types of pores.Cited as: Liang, Y., Lai, F., Dai, Y., Shi, H., Shi, G. An experimental study of imbibition process and fluid distribution in tight oil reservoir under different pressures and temperatures. Capillarity, 2021, 4(4): 66-75, doi: 10.46690/capi.2021.04.0

RNA-destabilizing Factor Tristetraprolin Negatively Regulates NF-kappa B Signaling

Tristetraprolin (TTP) is a CCCH zinc finger-containing protein that destabilizes mRNA by binding to an AU-rich element. Mice deficient in TTP develop a severe inflammatory syndrome mainly because of overproduction of tumor necrosis factor alpha. We report here that TTP also negatively regulates NF-kappa B signaling at the transcriptional corepressor level, by which it may repress inflammatory gene transcription. TTP expression inhibited NF-kappa B-dependent transcription. However, overexpression of TTP did not affect reporter mRNA stability. Instead, TTP functioned as a corepressor of p65/NF-kappa B. In support of this concept, we found that TTP physically interacted with the p65 subunit of NF-kappa B and was also associated with HDAC1, -3, and -7 in vivo. Treatment with histone deacetylase inhibitors or small interfering RNA induced HDAC1 or HDAC3 knockdown completely or partly abolished the inhibitory activity of TTP on NF-kappa B reporter activation. Consistently, chromatin immuno-precipitation showed decreased recruitment of HDAC1 and increased recruitment of CREB-binding protein on the Mcp-1 promoter in TTP(-/-) cells compared with wild-type cells. Moreover, overexpression of TTP blocked CREB-binding protein-induced acetylation of p65/NF-kappa B. Taken together, these data suggest that TTP may also function in vivo as a modulator in suppressing the transcriptional activity of NF-kappa B

Zonal Soil Type Determines Soil Microbial Responses to Maize Cropping and Fertilization.

Soil types heavily influence ecological dynamics. It remains controversial to what extent soil types shape microbial responses to land management changes, largely due to lack of in-depth comparison across various soil types. Here, we collected samples from three major zonal soil types spanning from cold temperate to subtropical climate zones. We examined bacterial and fungal community structures, as well as microbial functional genes. Different soil types had distinct microbial biomass levels and community compositions. Five years of maize cropping (growing corn or maize) changed the bacterial community composition of the Ultisol soil type and the fungal composition of the Mollisol soil type but had little effect on the microbial composition of the Inceptisol soil type. Meanwhile, 5 years of fertilization resulted in soil acidification. Microbial compositions of the Mollisol and Ultisol, but not the Inceptisol, were changed and correlated (P < 0.05) with soil pH. These results demonstrated the critical role of soil type in determining microbial responses to land management changes. We also found that soil nitrification potentials correlated with the total abundance of nitrifiers and that soil heterotrophic respiration correlated with the total abundance of carbon degradation genes, suggesting that changes in microbial community structure had altered ecosystem processes. IMPORTANCE Microbial communities are essential drivers of soil functional processes such as nitrification and heterotrophic respiration. Although there is initial evidence revealing the importance of soil type in shaping microbial communities, there has been no in-depth, comprehensive survey to robustly establish it as a major determinant of microbial community composition, functional gene structure, or ecosystem functioning. We examined bacterial and fungal community structures using Illumina sequencing, microbial functional genes using GeoChip, microbial biomass using phospholipid fatty acid analysis, as well as functional processes of soil nitrification potential and CO2 efflux. We demonstrated the critical role of soil type in determining microbial responses to land use changes at the continental level. Our findings underscore the inherent difficulty in generalizing ecosystem responses across landscapes and suggest that assessments of community feedback must take soil types into consideration. Author Video: An author video summary of this article is available

ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation

Multimodal recommendation aims to model user and item representations comprehensively with the involvement of multimedia content for effective recommendations. Existing research has shown that it is beneficial for recommendation performance to combine (user- and item-) ID embeddings with multimodal salient features, indicating the value of IDs. However, there is a lack of a thorough analysis of the ID embeddings in terms of feature semantics in the literature. In this paper, we revisit the value of ID embeddings for multimodal recommendation and conduct a thorough study regarding its semantics, which we recognize as subtle features of content and structures. Then, we propose a novel recommendation model by incorporating ID embeddings to enhance the semantic features of both content and structures. Specifically, we put forward a hierarchical attention mechanism to incorporate ID embeddings in modality fusing, coupled with contrastive learning, to enhance content representations. Meanwhile, we propose a lightweight graph convolutional network for each modality to amalgamate neighborhood and ID embeddings for improving structural representations. Finally, the content and structure representations are combined to form the ultimate item embedding for recommendation. Extensive experiments on three real-world datasets (Baby, Sports, and Clothing) demonstrate the superiority of our method over state-of-the-art multimodal recommendation methods and the effectiveness of fine-grained ID embeddings