Dependency Relationships-Enhanced Attentive Group Recommendation in HINs

Recommending suitable items to a group of users, commonly referred to as the group recommendation task, is becoming increasingly urgent with the development of group activities. The challenges within the group recommendation task involve aggregating the individual preferences of group members as the group's preferences and facing serious sparsity problems due to the lack of user/group-item interactions. To solve these problems, we propose a novel approach called Dependency Relationships-Enhanced Attentive Group Recommendation (DREAGR) for the recommendation task of occasional groups. Specifically, we introduce the dependency relationship between items as side information to enhance the user/group-item interaction and alleviate the interaction sparsity problem. Then, we propose a Path-Aware Attention Embedding (PAAE) method to model users' preferences on different types of paths. Next, we design a gated fusion mechanism to fuse users' preferences into their comprehensive preferences. Finally, we develop an attention aggregator that aggregates users' preferences as the group's preferences for the group recommendation task. We conducted experiments on two datasets to demonstrate the superiority of DREAGR by comparing it with state-of-the-art group recommender models. The experimental results show that DREAGR outperforms other models, especially HR@N and NDCG@N (N=5, 10), where DREAGR has improved in the range of 3.64% to 7.01% and 2.57% to 3.39% on both datasets, respectively.Comment: 14 pages, 9 figures, This paper has been submitted to IEEE Transactions on Knowledge and Data Engineerin

Dynamic Sealing Behavior of Sand Self-Juxtaposition Windows on a Trap-Bounding Fault in a Natural Gas Storage Site

AbstractAn understanding of across-fault seals is essential for planning an injection/production strategy for a fault-bounded gas storage site. In addition, it is more likely to permit lateral leakage for a fault with sand self-juxtaposition windows. This paper is aimed at identifying the dynamic sealing behaviors of a sand self-juxtaposition fault on the geological and gas injection timescales. Banzhongbei gas storage site, China, was taken as a target area, and fault seals and hydrocarbon distributions within the original reservoirs were studied. The results showed that across-fault pressure differences of 0.085~0.146 MPa (equivalent to 41.6~71.5 m oil-column and 27.0~46.4 m gas-column heights) were supported by sand self-juxtaposition windows on the B816 fault, and the resultant absolute permeability (5.97×10−2~5.69×10−1 mD) of the fault was nearly 3~4 orders of magnitude lower than the average absolute permeability of reservoirs (1.16×102 mD). Gas composition contrasts, between the original and injection gas coupled with dynamic pressure monitoring data, indicated that lateral leakage occurred across sand self-juxtaposition windows under the condition of high across-fault pressure difference. However, the low-permeability fault showed strong negative influence on the efficiency of fluid flow in the model calculations and prolongs the timescales of pressure-difference decayed as much as 5 orders of magnitude relative to those of nonfault model calculations. These modeled dynamic sealing behaviors of sand self-juxtaposition windows may lead to a better understanding of the relative retardation of across-fault gas flow by weak sealing faults on the gas injection/production timescale

Research on the Application Integration Model for the Agricultural Enterprise of Integrative Production and Marketing

International audienceTo integrate application systems under heterogeneous environments has gradually become a pressing demand for most agricultural enterprise. The vast diversity of implementation technologies increases the complexity of applications integration. With the aim of facilitating the development of integration solutions, we propose the specification of application integration. To achieve this, we analyzed the current situation of information, the environment of application systems, and the specific requirements of integration. We also studied the integration technologies. And then the ESB-SOA model for the agricultural enterprise of integrative production and marketing was proposed on the basis of a number of advanced technologies, such as ESB, SOA, EAI and Web Service. Based on ESB and SOA technologies, the model is able to utilize and integrate effectively various service resources on the geographically distributed computing environments. And through intelligent agent technologies, it supports cooperation and coordination mechanisms for the service activities. To exemplify and validate the profile, a case study is presented in which the proposed profile is used

Research and Application of Data Security for Mobile Devices

International audienceMobile devices have been increasingly become important tools for the information system application in agriculture, but the safe problem that follow also results in risks of economy lose. Considering above problems and combining the application of an agriculture chain logistics management system, a series of jobs have been done. This paper presents a data security solution which encrypts the configuration files to protect sensitive information and uses SSL protocol to protect the network transmission security. At present, this solution has been modular integrated with the agriculture chain logistics management system. This paper also provides reference for data security problems of other mobile devices

Quantitative analysis using intraoperative contrast-enhanced ultrasound in adult-type diffuse gliomas with isocitrate dehydrogenase mutations: association between hemodynamics and molecular features

Purpose The relationship between contrast-enhanced ultrasound (CEUS) hemodynamics and the molecular biomarkers of adult-type diffuse gliomas, particularly isocitrate dehydrogenase (IDH), remains unclear. This study was conducted to provide a comprehensive description of the vascularization of adult-type diffuse gliomas using quantitative indicators. Additionally, it was designed to identify any variables with the potential to intraoperatively predict IDH mutation status. Methods This prospective study enrolled patients with adult-type diffuse gliomas between November 2021 and September 2022. Intraoperative CEUS was performed, and CEUS videos were recorded for 90-second periods. Hemodynamic parameters, including the peak enhancement (PE) difference, were calculated based on the time-intensity curve of the region of interest. A differential analysis was performed on the CEUS parameters with respect to molecular biomarkers and grades. Receiver operating characteristic curves for various parameters were analyzed to evaluate the ability of those parameters to predict IDH mutation status. Results Sixty patients with adult-type diffuse gliomas were evaluated. All hemodynamic parameters, apart from rising time, demonstrated significant differences between IDH-mutant and IDH-wildtype adult-type diffuse gliomas. The PE difference emerged as the optimal indicator for differentiating between IDH-wildtype and IDH-mutant gliomas, with an area under the curve of 0.958 (95% confidence interval, 0.406 to 0.785). Additionally, the hemodynamic parameters revealed significant differences across both grades and types of adult-type diffuse gliomas. Conclusion Hemodynamic parameters can be used intraoperatively to effectively distinguish between IDH-wildtype and IDH-mutant adult-type diffuse gliomas. Additionally, quantitative CEUS equips neurosurgeons with dynamic perfusion information for various types and grades of adult-type diffuse gliomas

Rapid intraoperative multi-molecular diagnosis of glioma with ultrasound radio frequency signals and deep learningResearch in context

Summary: Background: Molecular diagnosis is crucial for biomarker-assisted glioma resection and management. However, some limitations of current molecular diagnostic techniques prevent their widespread use intraoperatively. With the unique advantages of ultrasound, this study developed a rapid intraoperative molecular diagnostic method based on ultrasound radio-frequency signals. Methods: We built a brain tumor ultrasound bank with 169 cases enrolled since July 2020, of which 43483 RF signal patches from 67 cases with a pathological diagnosis of glioma were a retrospective cohort for model training and validation. IDH1 and TERT promoter (TERTp) mutations and 1p/19q co-deletion were detected by next-generation sequencing. We designed a spatial–temporal integration model (STIM) to diagnose the three molecular biomarkers, thus establishing a rapid intraoperative molecular diagnostic system for glioma, and further analysed its consistency with the fifth edition of the WHO Classification of Tumors of the Central Nervous System (WHO CNS5). We tested STIM in 16-case prospective cohorts, which contained a total of 10384 RF signal patches. Two other RF-based classical models were used for comparison. Further, we included 20 cases additional prospective data for robustness test (ClinicalTrials.gov NCT05656053). Findings: In the retrospective cohort, STIM achieved a mean accuracy and AUC of 0.9190 and 0.9650 (95% CI, 0.94–0.99) respectively for the three molecular biomarkers, with a total time of 3 s and a 96% match to WHO CNS5. In the prospective cohort, the diagnostic accuracy of STIM is 0.85 ± 0.04 (mean ± SD) for IDH1, 0.84 ± 0.05 for TERTp, and 0.88 ± 0.04 for 1p/19q. The AUC is 0.89 ± 0.02 (95% CI, 0.84–0.94) for IDH1, 0.80 ± 0.04 (95% CI, 0.71–0.89) for TERTp, and 0.85 ± 0.06 (95% CI, 0.73–0.98) for 1p/19q. Compared to the second best available method based on RF signal, the diagnostic accuracy of STIM is improved by 16.70% and the AUC is improved by 19.23% on average. Interpretation: STIM is a rapid, cost-effective, and easy-to-manipulate AI method to perform real-time intraoperative molecular diagnosis. In the future, it may help neurosurgeons designate personalized surgical plans and predict survival outcomes. Funding: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section

DataSheet1_Vibratome sectioning of tumors to evaluate the interactions between nanoparticles and the tumor microenvironment ex-vivo.docx

Nanoparticles have been investigated as drug carriers and promising agents for cancer therapy. However, the tumor microenvironment (TME), which is formed by the tumor, is considered a barrier for nanocarriers to enter the internal tumor tissue. Therefore, the evaluation of the biological distribution of nanocarriers in TME can provide useful information on their role in tumor-targeted drug delivery. Although the tumor-bearing mouse model is commonly used to investigate the distribution of nanocarriers in the TME, there is currently a lack of a testing system to predict the distribution of nanocarriers in tumor tissues, especially in patients. This study revealed that the macrophages and dendritic cells (DCs) were more distributed in the peripheral part than the central part of the tumor, which might be an obstacle to the uniform distribution of nanoparticles in the tumor. In addition, the cellular uptake of gold nanoparticles (AuNR and AuNS) in macrophages and DCs cell lines (RAW264.7 and DC1.2) was markedly different from that in the TME. Hence, the study model of the interaction between nanoparticles and macrophages and DCs has an important impact on the accuracy of the results. The vibratome sections of tumor tissues preserved the spatial distribution of immune cells and tumor cells, and had very little effects on their morphologies and activities. More importantly, we found that the distribution of nanocarriers in vibratome sections was similar to that in tumors in vivo. In all, ex vivo analysis using vibratome sections of tumor tissues provides a more convenient and stable method for elucidating the influences of TME on the distribution of nanocarriers.</p