Incorporating Intra-Class Variance to Fine-Grained Visual Recognition

Fine-grained visual recognition aims to capture discriminative characteristics amongst visually similar categories. The state-of-the-art research work has significantly improved the fine-grained recognition performance by deep metric learning using triplet network. However, the impact of intra-category variance on the performance of recognition and robust feature representation has not been well studied. In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition. Through partitioning training images within each category into a few groups, we form the triplet samples across different categories as well as different groups, which is called Group Sensitive TRiplet Sampling (GS-TRS). Accordingly, the triplet loss function is strengthened by incorporating intra-class variance with GS-TRS, which may contribute to the optimization objective of triplet network. Extensive experiments over benchmark datasets CompCar and VehicleID show that the proposed GS-TRS has significantly outperformed state-of-the-art approaches in both classification and retrieval tasks.Comment: 6 pages, 5 figure

Study on multicomponent composite anti-corrosion cement slurry system suitable for ultra-high temperature acid gas wells

In the field of oil cementing, corrosion has always been a major problem that perplexes researchers. In the past, the research mainly focused on solving the corrosion problem of cement stone with temperature below 150°C, and there was a lack of corrosion research cases for ultra-high temperature. In addition, the gas channeling problem in the cementing of ultra-high temperature acid gas wells cannot be ignored, which further increases the difficulty in the design of anti-corrosion cement slurry system. Therefore, from the perspective of anti-corrosion, gas channeling and high temperature resistance, this paper uses hydroxyapatite blast furnace slag and functional temperature resistant and anti-corrosion composite emulsion as anti-corrosion additives to build a multi-component composite ultra-high temperature anti-corrosion cement slurry system with good engineering performance and a density range of 1.9 g/cm3-2.4 g/cm3, and analyzes its microstructure and phase composition. The corrosion inhibition mechanism of multicomponent composite cement paste was discussed

NB-IoT Uplink Synchronization by Change Point Detection of Phase Series in NTNs

Non-Terrestrial Networks (NTNs) are widely recognized as a potential solution to achieve ubiquitous connections of Narrow Bandwidth Internet of Things (NB-IoT). In order to adopt NTNs in NB-IoT, one of the main challenges is the uplink synchronization of Narrowband Physical Random Access procedure which refers to the estimation of time of arrival (ToA) and carrier frequency offset (CFO). Due to the large propagation delay and Doppler shift in NTNs, traditional estimation methods for Terrestrial Networks (TNs) can not be applied in NTNs directly. In this context, we design a two stage ToA and CFO estimation scheme including coarse estimation and fine estimation based on abrupt change point detection (CPD) of phase series with machine learning. Our method achieves high estimation accuracy of ToA and CFO under the low signal-noise ratio (SNR) and large Doppler shift conditions and extends the estimation range without enhancing Random Access preambles

Reasonable production allocation model of gas wells for deep tight gas reservoirs with the edge water

Deep tight gas reservoirs are one of the important unconventional gas reservoirs. Deep burial, tight reservoirs have many characteristics, including diverse accumulation patterns, multiple accumulation regulations, low natural energy generation, complex gas–water relationship, and intricate seepage mechanism. These features of gas reservoirs put forward the requirement for new methods for a reasonable production allocation of horizontal wells and optimization of such allocations from the perspective of stress sensitivity. While CO2 huff-and-puff-based models, numerical simulation models, and thermos-hydrodynamic models have been built to solve these issues, there is still a lack of theoretical guidance for reasonable production allocation, especially with the edge-water problem. Here, we present a new one-dimensional mathematical and physical model to capture the stable movement of the gas–water interface in deep tight edge-water gas reservoirs. Our results show that there is a starting pressure in deep tight gas reservoirs. The starting pressure gradient increases with the growth of water saturation, which is far greater than the starting pressure gradient of medium, shallow gas reservoirs under the same water saturation. In addition, by considering the stable movement of the gas–water interface under the starting pressure, we found that the gas well has a larger upper limit of production differential pressure, a smaller seepage velocity, and a lower upper limit of production allocation. Finally, we make a comparison between our model results and production characteristics of real gas wells and find a consistency between the model results with real data. Our model provides a theoretical framework for reasonable production allocation of gas wells in deep tight gas reservoirs with the edge water

Progress in Preparation and Application of Anthocyanin-Starch Complexes: A Review

Anthocyanins are natural colorants that have attracted increasing attention due to their wide color range, non-toxicity and health benefits. Although anthocyanins have great application potential in the food and pharmaceutical industries, their application is limited due to the relative instability. Starch is considered as a good protective agent for anthocyanins, which can improve the stability of anthocyanins during storage. In recent years, many studies have combined the two compounds by different methods such as physical and chemical methods. This can not only enhance the stability of anthocyanins, but also improve the mechanical properties of starch, which will result in better application of starch and anthocyanins in drug delivery, biomedicine, agriculture, and food production. The basic structural characteristics of anthocyanins and starch, and the various methods for preparing anthocyanin-starch complexes are summarized herein. Also, the effects of anthocyanin-starch interactions on anthocyanin stability, bioavailability and antioxidant activity and on starch crystallinity, gelatinization properties, mechanical properties and digestibility are reviewed, and the current progress in the application of anthocyanin-starch complexes is outlined. It is hoped that this review will provide a reference for future research on the preparation and application of anthocyanin-starch complexes

Incorporating inflammatory biomarkers into a prognostic risk score in patients with non-ischemic heart failure: a machine learning approach

ObjectivesInflammation is involved in the mechanisms of non-ischemic heart failure (NIHF). We aimed to investigate the prognostic value of 21 inflammatory biomarkers and construct a biomarker risk score to improve risk prediction for patients with NIHF.MethodsPatients diagnosed with NIHF without infection during hospitalization were included. The primary outcome was defined as all-cause mortality and heart transplantations. We used elastic net Cox regression with cross-validation to select inflammatory biomarkers and construct the best biomarker risk score model. Discrimination, calibration, and reclassification were evaluated to assess the predictive value of the biomarker risk score.ResultsOf 1,250 patients included (median age, 53 years, 31.9% women), 436 patients (34.9%) experienced the primary outcome during a median of 2.8 years of follow-up. The final biomarker risk score included high-sensitivity C-reactive protein-to-albumin ratio (CAR) and red blood cell distribution width-standard deviation (RDW-SD), both of which were 100% selected in 1,000 times cross-validation folds. Incorporating the biomarker risk score into the best basic model improved the discrimination (ΔC-index = 0.012, 95% CI 0.003–0.018) and reclassification (IDI, 2.3%, 95% CI 0.7%–4.9%; NRI, 17.3% 95% CI 6.4%–32.3%) in risk identification. In the cross-validation sets, the mean time-dependent AUC ranged from 0.670 to 0.724 for the biomarker risk score and 0.705 to 0.804 for the basic model with a biomarker risk score, from 1 to 8 years. In multivariable Cox regression, the biomarker risk score was independently associated with the outcome in patients with NIHF (HR 1.76, 95% CI 1.49–2.08, p < 0.001, per 1 score increase).ConclusionsAn inflammatory biomarker-derived risk score significantly improved prognosis prediction and risk stratification, providing potential individualized therapeutic targets for NIHF patients

Extracellular vesicle-mediated communication between CD8+ cytotoxic T cells and tumor cells

Tumors pose a significant global public health challenge, resulting in numerous fatalities annually. CD8+ T cells play a crucial role in combating tumors; however, their effectiveness is compromised by the tumor itself and the tumor microenvironment (TME), resulting in reduced efficacy of immunotherapy. In this dynamic interplay, extracellular vesicles (EVs) have emerged as pivotal mediators, facilitating direct and indirect communication between tumors and CD8+ T cells. In this article, we provide an overview of how tumor-derived EVs directly regulate CD8+ T cell function by carrying bioactive molecules they carry internally and on their surface. Simultaneously, these EVs modulate the TME, indirectly influencing the efficiency of CD8+ T cell responses. Furthermore, EVs derived from CD8+ T cells exhibit a dual role: they promote tumor immune evasion while also enhancing antitumor activity. Finally, we briefly discuss current prevailing approaches that utilize functionalized EVs based on tumor-targeted therapy and tumor immunotherapy. These approaches aim to present novel perspectives for EV-based tumor treatment strategies, demonstrating potential for advancements in the field

Critical transition of soil microbial diversity and composition triggered by plant rhizosphere effects

Over the years, microbial community composition in the rhizosphere has been extensively studied as the most fascinating topic in microbial ecology. In general, plants affect soil microbiota through rhizodeposits and changes in abiotic conditions. However, a consensus on the response of microbiota traits to the rhizosphere and bulk soils in various ecosystems worldwide regarding community diversity and structure has not been reached yet. Here, we conducted a meta-analysis of 101 studies to investigate the microbial community changes between the rhizosphere and bulk soils across various plant species (maize, rice, vegetables, other crops, herbaceous, and woody plants). Our results showed that across all plant species, plant rhizosphere effects tended to reduce the rhizosphere soil pH, especially in neutral or slightly alkaline soils. Beta-diversity of bacterial community was significantly separated between into rhizosphere and bulk soils. Moreover, r-strategists and copiotrophs (e.g. Proteobacteria and Bacteroidetes) enriched by 24-27% in the rhizosphere across all plant species, while K-strategists and oligotrophic (e.g. Acidobacteria, Gemmatimonadete, Nitrospirae, and Planctomycetes) decreased by 15-42% in the rhizosphere. Actinobacteria, Firmicutes, and Chloroflexi are also depleted by in the plant rhizosphere compared with the bulk soil by 7-14%. The Actinobacteria exhibited consistently negative effect sizes across all plant species, except for maize and vegetables. In Firmicutes, both herbaceous and woody plants showed negative responses to rhizosphere effects, but those in maize and rice were contrarily enriched in the rhizosphere. With regards to Chloroflexi, apart from herbaceous plants showing a positive effect size, the plant rhizosphere effects were consistently negative across all other plant types. Verrucomicrobia exhibited a significantly positive effect size in maize, whereas herbaceous plants displayed a negative effect size in the rhizosphere. Overall, our meta-analysis exhibited significant changes in microbial community structure and diversity responding to the plant rhizosphere effects depending on plant species, further suggesting the importance of plant rhizosphere to environmental changes influencing plants and subsequently their controls over the rhizosphere microbiota related to nutrient cycling and soil health