Browse Articles

Service recommendation method based on text view and interaction view

Abstract With the increasing prosperity of web service-sharing platforms, more and more software developers are integrating and reusing Web services when developing applications. This approach not only meets the needs of developers but also is cost-effective and widely used in the field of software development. Usually, software developers can browse, evaluate, and select corresponding Web services from a web service-sharing platform to create various applications with rich functionality. However, a large number of candidate Web services have placed a heavy burden on the selection decisions of software developers. Existing web service recommendation systems often face two challenges. Firstly, developers discover services by inputting development requirements, but the user’s input is arbitrary and can not fully reflect the user’s intention. Secondly, the application service interaction record is too sparse, reaching 99.9%, making it particularly difficult to extract services that meet the requirements. To address the above challenges, in this paper, we propose a service recommendation method based on text and interaction views (SRTI). Firstly, SRTI employs graph neural network algorithms to deeply mine the historical records, extract the features of applications and services, and calculate their preferences. Secondly, SRTI uses Transformer to analysis develop requirements and uses fully connected neural networks to deeply mine the matching degree between candidate services and development requirements. Finally, we integrate the above two to obtain the final service list. Extensive experiments on real-world datasets have shown that SRTI outperforms several state-of-the-art methods in service recommendation.

Machine learning of clinical phenotypes facilitates autism screening and identifies novel subgroups with distinct transcriptomic profiles

Enhanced variable step sizes perturb and observe MPPT control to reduce energy loss in photovoltaic systems

Psychometric properties of the Arabic version of the Eco guilt and Eco grief scales

Design and experiment of automatic grasping manipulator for side-mounted garbage truck

Traditional Chinese medicine-based therapeutics for Pediatric pneumonia-related acute lung injury and acute respiratory distress syndrome

Interim results of exoskeletal wearable robot for gait recovery in subacute stroke patients

Synthesis of poly (acrylic acid) modified graphene/MoS2 heterostructure-based composite: an effective removal of Pb(II), Cd(II) and Zn(II) from wastewater

Identification of patients at risk for pancreatic cancer in a 3-year timeframe based on machine learning algorithms

Abstract Early detection of pancreatic cancer (PC) remains challenging largely due to the low population incidence and few known risk factors. However, screening in at-risk populations and detection of early cancer has the potential to significantly alter survival. In this study, we aim to develop a predictive model to identify patients at risk for developing new-onset PC at two and a half to three year time frame . We used the Electronic Health Records (EHR) of a large medical system from 2000 to 2021 (N = 537,410). The EHR data analyzed in this work consists of patients’ demographic information, diagnosis records, and lab values, which are used to identify patients who were diagnosed with pancreatic cancer and the risk factors used in the machine learning algorithm for prediction. We identified 73 risk factors of pancreatic cancer with the Phenome-wide Association Study (PheWAS) on a matched case–control cohort. Based on them, we built a large-scale machine learning algorithm based on EHR. A temporally stratified validation based on patients not included in any stage of the training of the model was performed. This model showed an AUROC at 0.742 [0.727, 0.757] which was similar in both the general population and in a subset of the population who has had prior cross-sectional imaging. The rate of diagnosis of pancreatic cancer in those in the top 1 percentile of the risk score was 6 folds higher than the general population. Our model leverages data extracted from a 6-month window of time in the electronic health record to identify patients at nearly sixfold higher than baseline risk of developing pancreatic cancer 2.5–3 years from evaluation. This approach offers an opportunity to define an enriched population entirely based on static data, where current screening may be recommended.

Prognostic factors in pediatrics TAPVC: a 10-year retrospective study

Social needs and hospital readmission in persons living with HIV

3D geological model reconstruction and collapse risk evaluation of old goaf in Dunqiu iron mine

A synergistic approach to enhanced oil recovery by combining in-situ surfactant production and wettability alteration in carbonate reservoirs

A lipid metabolism related gene signature predicts postoperative recurrence in pancreatic cancer through multicenter cohort validation

Network performance assessment for public health emergency response: multi-case study of SARS, H1N1 and COVID-19 in China

Author Correction: New results in stereopsis and Listing’s law

Utilizing parthenocarpic gynoecious beit alpha cucumber inbreds for their heterotic potential under different poly-net house environments

Evaluation of crop phenology using remote sensing and decision support system for agrotechnology transfer

Efficacy of slow daily home hemodialysis with internal convection on removal of uremic toxins using the Physidia S3 monitor

Design and validation of a semi-quantitative microneutralization assay for human Metapneumovirus A1 and B1 subtypes

Abstract Since 2001, human Metapneumovirus has been a significant cause of human respiratory disease worldwide, and no vaccine or preventive treatment is currently available. The ELISA-based live virus microneutralization assay is a method to detect neutralizing antibodies against a target pathogen. The aim of this study was to demonstrate the suitability of this approach to quantifying neutralizing antibodies against A1 and B1 virus subtypes in human serum samples. To standardize and validate this microneutralization assay, we carried out analytical procedures according to the International Council of Harmonization guidelines; these procedures are described in detail. In addition, we compared the validated method with the indirect ELISA, and confirmed that the ELISA-based microneutralization assay provides reliable, accurate and reproducible results. The use of this high-throughput method for large-scale serological studies could effectively support the evaluation of the immunogenicity of new vaccines, thereby improving therapeutical strategies against human Metapneumovirus.