USING SOCIAL NETWORK ANALYSIS AS A STRATEGY FOR E-COMMERCE RECOMMENDATION

Recommender agents are being widely used by E-commerce business to help customers make decisions from a large amount of choices. To improve the performance of recommendation agents, three main approaches (content-based approaches, collaborative approaches and hybrid approaches) have been proposed to address recommendation problem whose basic idea is to discover similarity of items and users and predicate users’ preference toward a set of items. This provides potential for using social network analysis to make recommendations since social network analysis can be used to investigate the relationships of customers. In this research, we illustrate the concepts of social network analysis and how it can be employed to make better recommendations in E-commerce context. Application and research opportunities are presented

The Whole Pathological Slide Classification via Weakly Supervised Learning

Due to its superior efficiency in utilizing annotations and addressing gigapixel-sized images, multiple instance learning (MIL) has shown great promise as a framework for whole slide image (WSI) classification in digital pathology diagnosis. However, existing methods tend to focus on advanced aggregators with different structures, often overlooking the intrinsic features of H\&E pathological slides. To address this limitation, we introduced two pathological priors: nuclear heterogeneity of diseased cells and spatial correlation of pathological tiles. Leveraging the former, we proposed a data augmentation method that utilizes stain separation during extractor training via a contrastive learning strategy to obtain instance-level representations. We then described the spatial relationships between the tiles using an adjacency matrix. By integrating these two views, we designed a multi-instance framework for analyzing H\&E-stained tissue images based on pathological inductive bias, encompassing feature extraction, filtering, and aggregation. Extensive experiments on the Camelyon16 breast dataset and TCGA-NSCLC Lung dataset demonstrate that our proposed framework can effectively handle tasks related to cancer detection and differentiation of subtypes, outperforming state-of-the-art medical image classification methods based on MIL. The code will be released later

Boundary-to-Solution Mapping for Groundwater Flows in a Toth Basin

In this paper, the authors propose a new approach to solving the groundwater flow equation in the Toth basin of arbitrary top and bottom topographies using deep learning. Instead of using traditional numerical solvers, they use a DeepONet to produce the boundary-to-solution mapping. This mapping takes the geometry of the physical domain along with the boundary conditions as inputs to output the steady state solution of the groundwater flow equation. To implement the DeepONet, the authors approximate the top and bottom boundaries using truncated Fourier series or piecewise linear representations. They present two different implementations of the DeepONet: one where the Toth basin is embedded in a rectangular computational domain, and another where the Toth basin with arbitrary top and bottom boundaries is mapped into a rectangular computational domain via a nonlinear transformation. They implement the DeepONet with respect to the Dirichlet and Robin boundary condition at the top and the Neumann boundary condition at the impervious bottom boundary, respectively. Using this deep-learning enabled tool, the authors investigate the impact of surface topography on the flow pattern by both the top surface and the bottom impervious boundary with arbitrary geometries. They discover that the average slope of the top surface promotes long-distance transport, while the local curvature controls localized circulations. Additionally, they find that the slope of the bottom impervious boundary can seriously impact the long-distance transport of groundwater flows. Overall, this paper presents a new and innovative approach to solving the groundwater flow equation using deep learning, which allows for the investigation of the impact of surface topography on groundwater flow patterns

Fetal-maternal interactions during pregnancy: a ‘three-in-one’ perspective

A successful human pregnancy requires the maternal immune system to recognize and tolerate the semi-allogeneic fetus, allowing for appropriate trophoblasts invasion and protecting the fetus from invading pathogens. Therefore, maternal immunity is critical for the establishment and maintenance of pregnancy, especially at the maternal-fetal interface. Anatomically, the maternal-fetal interface has both maternally- and fetally- derived cells, including fetal originated trophoblasts and maternal derived immune cells and stromal cells. Besides, a commensal microbiota in the uterus was supposed to aid the unique immunity in pregnancy. The appropriate crosstalk between fetal derived and maternal originated cells and uterine microbiota are critical for normal pregnancy. Dysfunctional maternal-fetal interactions might be associated with the development of pregnancy complications. This review elaborates the latest knowledge on the interactions between trophoblasts and decidual immune cells, highlighting their critical roles in maternal-fetal tolerance and pregnancy development. We also characterize the role of commensal bacteria in promoting pregnancy progression. Furthermore, this review may provide new thought on future basic research and the development of clinical applications for pregnancy complications

The Clinical Relevance of Serum NDKA, NMDA, PARK7, and UFDP Levels with Phlegm-Heat Syndrome and Treatment Efficacy Evaluation of Traditional Chinese Medicine in Acute Ischemic Stroke

According to the methods of Patient-Reported Outcome (PRO) based on the patient reports internationally and referring to U.S. Food and Drug Administration (FDA) guide, some scholars developed this PRO of stroke which is consistent with China’s national conditions, and using it the feel of stroke patients was introduced into the clinical efficacy evaluation system of stoke. “Ischemic Stroke TCM Syndrome Factor Diagnostic Scale (ISTSFDS)” and “Ischemic Stroke TCM Syndrome Factor Evaluation Scale (ISTSFES)” were by “Major State Basic Research Development Program of China (973 Program) (number 2003CB517102).” ISTSFDS can help to classify and diagnose the CM syndrome reasonably and objectively with application of syndrome factors. Six syndrome factors, internal-wind syndrome, internal-fire syndrome, phlegm-dampness syndrome, blood-stasis syndrome, qi-deficiency syndrome, and yin-deficiency syndrome, were included in ISTSFDS and ISTSFES. TCM syndrome factor was considered to be present if the score was greater than or equal to 10 according to ISTSFDS. In our study, patients with phlegm-heat syndrome were recruited, who met the diagnosis of both “phlegm-dampness” and “internal-fire” according to ISTSFDS. ISTSFES was used to assess the syndrome severity; in our study it was used to assess the severity of phlegm-heat syndrome (phlegm-heat syndrome scores = phlegm-dampness syndrome scores + internal-fire syndrome scores)

Periphytic biofilms accumulate manganese, intercepting its emigration from paddy soil

Manganese (Mn) in acidic paddy soil has large potential in emigrating from the soil and pollute adjacent ecosystems. Single microorganisms modulate the biogeochemistry process of Mn via redox reactions, while the roles of microbial aggregates (e.g. periphytic biofilm) in modulating its biogeochemical cycle is poorly constrained. Here we collected a series of periphytic biofilms from acidic paddy fields in China to explore how periphytic biofilm regulates Mn behavior in paddy fields. We found that periphytic biofilms have large Mn accumulation potential: Mn contents in periphytic biofilm ranged from 176 ± 38 to 797 ± 271 mg/kg, which were 1.2-4.5 folds higher than that in the corresponding soils. Field experiments verified the Mn accumulation potential, underlining the biofilms function as natural barriers to intercept Mn emigrating from soil. Extracellular polymeric substances, especially the protein component, mediated adsorption was the main mechanism behind Mn accumulation by periphytic biofilm. Microorganisms in periphytic biofilms in general appeared to have inhibitory effects on Mn accumulation. Climatic conditions and nutrients in floodwater and soil affect the microorganisms, thus indirectly affecting Mn accumulation in periphytic biofilms. This study provides quantitative information on the extent to which microbial aggregates modulate the biogeochemistry of Mn in paddy fields

DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing

In the field of meteorology, the global radar network is indispensable for detecting weather phenomena and offering early warning services. Nevertheless, radar data frequently exhibit anomalies, including gaps and clutter, arising from atmospheric refraction, equipment malfunctions, and other factors, resulting in diminished data quality. Traditional radar blockage correction methods, such as employing approximate radial information interpolation and supplementing missing data, often fail to effectively exploit potential patterns in massive radar data, for the large volume of data precludes a thorough analysis and understanding of the inherent complex patterns and dependencies through simple interpolation or supplementation techniques. Fortunately, edge computing possesses certain data processing capabilities and cloud center boasts substantial computational power, which together can collaboratively offer timely computation and storage for the correction of radar beam blockage. To this end, an edge-cloud collaborative driven deep learning model named DenMerD is proposed in this paper, which includes dense connection module and merge distribution (MD) unit. Compared to existing models such as RC-FCN, DenseNet, and VGG, this model greatly improves key performance metrics, with 30.7% improvement in Critical Success Index (CSI), 30.1% improvement in Probability of Detection (POD), and 3.1% improvement in False Alarm Rate (FAR). It also performs well in the Structure Similarity Index Measure (SSIM) metrics compared to its counterparts. These findings underscore the efficacy of the design in improving feature propagation and beam blockage accuracy, and also highlights the potential and value of mobile edge computing in processing large-scale meteorological data