MODIFIED SCHEME BASED ON SEMI-ANALYTIC APPROACH FOR COMPUTING NON-PROBABILISTIC RELIABILITY INDEX

A new computation scheme proposed to tackle commensurate problems is devel- oped by modifying the semi-analytic approach for minimizing computational complexity. Using the proposed scheme, the limit state equations, usually referred to as the failure surface, are obtained from transformation of an interval variable to a normalized one. In order to minimize the computational cost, two algorithms for optimizing the calculation steps have been proposed. The monotonicity of the objective function can be determined from narrowing the scope of interval variables in normalized infinite space by incorporating the algorithms into the computational scheme. Two examples are used to illustrate the operation and computational efficiency of the approach. The results of these examples show that the proposed algorithms can greatly reduce the computation complexity without sacrificing the computational accuracy. The advantage of the proposed scheme can be even more efficient for analyzing sophistic structures.

Fast assignment reduction in inconsistent incomplete decision systems

This paper focuses on fast algorithm for computing the assignment reduct in inconsistent incomplete decision systems. It is quite inconvenient to judge the assignment reduct directly ac-cording to its definition. We propose the judgment theorem for the assignment reduct in the inconsistent incomplete decision system, which greatly simplifies judging this type reduct. On such basis, we derive a novel attribute significance measure and construct the fast assignment reduction algorithm （F-ARA）, intended for com-puting the assignment reduct in inconsistent incomplete decision systems. Final y, we make a comparison between F-ARA and the discernibility matrix-based method by experiments on 13 Univer-sity of California at Irvine （UCI） datasets, and the experimental results prove that F-ARA is efficient and feasible.

Numerical Simulation of Dynamic Electro-Mechanical Response of Ionic Polymer-Metal Composites

Ionic Polymer-Metal Composites （IPMC） is an emerging class of Electro-Active Polymer （EAP） materials. IPMC has attractive features, such as high sensitivity and light weight, which are useful for developing novel designs in the fields of bionic actuators, artificial muscles and dynamic sensors. A Finite Element （FE） model was developed for simulating the dynamic electro-mechanical response of an IPMC structure under an external voltage input. A lumped Resisto^Capacitor （RC） model was used to describe the voltage-to-current relationship of a Nation IPMC film for the computation of electric field intensity. Moreover, the viscoelastic property of the IPMC film was considered in the model and the non-uniform bending behavior was also taken into account. Based on the proposed model and the assumption that the thicknesses of the two electrodes are the same and uniform, the optimal coating thickness of the IPMC electrode was determined. It was demonstrated that the dynamic electro-mechanical response of the IPMC structure can be predicted by the proposed FE model, and the simulation results were in good agreement with the experimental findings.

Medical Additive Manufacturing: From a Frontier Technology to the Research and Development of Products

1.Research and development(R&D)and the challenges of raw materials for medical additive manufacturing Raw materials for medical additive manufacturing have a wide range of commonalities that are also seen in many other fields,making them an important basis in the field of three-dimensional(3D)printing.Problems and challenges related to material types,powder properties,formability,viscoelasticity,and so forth also share common features.For example,many metal materials are used in the field of aviation,while metals,polymers,and inorganic materials are used in the field of biomedicine.The most widely used materials in biomedicine are biocompatible.Various homogeneous and non-homogeneous composites are also available for 3D printing,and impose an additional challenge in additive manufacturing;the use of heterogeneous composites in 3D printing is particularly challenging.

Speed-up Multi-modal Near Duplicate Image Detection

Near-duplicate image detection is a necessary operation to refine image search results for efficient user exploration. The existences of large amounts of near duplicates require fast and accurate automatic near-duplicate detection methods. We have designed a coarse-to-fine near duplicate detection framework to speed-up the process and a multi-modal integra-tion scheme for accurate detection. The duplicate pairs are detected with both global feature (partition based color his-togram) and local feature (CPAM and SIFT Bag-of-Word model). The experiment results on large scale data set proved the effectiveness of the proposed design.

The Effect of Intensive Psychological Nursing on the Mood and Solutions of Spinal Tuberculosis Patients

Objective:To investigate the effect of intensive psycho1ogical nursing on the mood and coping ways of spinal tuberculosis patients.Methods:The clinical data of 102 patients undergoing spinal tuberculosis surgery in our hospital from February 2017 to January 2020 were retrospectively analyzed.A11 the cases were grouped according to different nursing plans,patients who received routine care were included in the control group(n=50),and the ones with intensive psychological care were included in the observation group(n=52).Compare negative emotions after nursing[assessment using self-assessment scale of anxiety(SAS),depression self-assessment scale(SDS)]and solutions[assessment using medical response questionnaire(MCMQ)]of the two groups.Results:After nursing,the SAs,SDs scores,avoidance and yield scores of the two groups were reduced,and the coping scores were increased,and the change of the observation group was greater than that of the control group,the difference was statistically significant(P<0.05).Conclusion:Spinal tuberculosis surgery patients were treated with intensive psychological care,which can relieve patients'negative emotions,improve solutions and are worthy of clinical use.

Study on the Value of the Theory of Protection Motivation in the Nursing of Spinal Fracture to the Quality of Life

Objective:To analyze the effect of protection motivation theory on the quality of life of patients with spinal fracture.Methods:From August 2019 to September 2020,72 patients with spinal fracture were selected and randomly divided into two groups.The routine nursing group was the routine nursing group,and the combined nursing with the theory of protective motivation was the dynamic nursing group.Results:The hospitalization time,detumescence time,healing time and muscle strength recovery time of group A were shorter than those of group B(P<0.05).The VAS score and Barthel index score of the dynamic group were better than those of the conventional group(P<0.05);The score of SF-36 in the group A was higher than that in the group B(P<0.05).Conclusions:The application of protection motivation theory in the nursing of patients with spinal fracture can shorten the healing time of fracture,promote the recovery of muscle strength,relieve the pain of fracture,and then improve the ability of daily life and quality of life of patients.

On Time-Aware Cross-Blockchain Data Migration

With the widespread adoption of blockchain applications, the imperative for seamless data migration among decentralized applications has intensified. This necessity arises from various factors, including the depletion of blockchain disk space, transitions between blockchain systems, and specific requirements such as temporal data analysis. To meet these challenges and ensure the sustained functionality of applications, it is imperative to conduct time-aware cross-blockchain data migration. This process is designed to facilitate the smooth iteration of decentralized applications and the construction of a temporal index for historical data, all while preserving the integrity of the original data. In various application scenarios, this migration task may encompass the transfer of data between multiple blockchains, involving movements from one chain to another, from one chain to several chains, or from multiple chains to a single chain. However, the success of data migration hinges on the careful consideration of factors such as the reliability of the data source, data consistency, and migration efficiency. This paper introduces a time-aware cross-blockchain data migration approach tailored to accommodate diverse application scenarios, including migration between multiple chains. The proposed solution integrates a collective mechanism for controlling, executing, and storing procedures to address the complexities of data migration, incorporating elements such as transaction classification and matching. Extensive experiments have been conducted to validate the efficacy of the proposed approach.

Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization

Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern recognition.It has been widely used and studied in the multi-view clustering tasks because of its effectiveness.This study proposes a general semi-supervised multi-view nonnegative matrix factorization algorithm.This algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different views.Two specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework:Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization(GDCMVNMF)and Extended Multi-View Constrained Nonnegative Matrix Factorization(ExMVCNMF).The intrinsic connection between these two specific implementations is discussed,and the optimization based on multiply update rules is presented.Experiments on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.

MR-DBSCAN： a scalable MapReduce-based DBSCAN algorithm for heavily skewed data

DBSCAN （density-based spatial clustering of ap- plications with noise） is an important spatial clustering tech- nique that is widely adopted in numerous applications. As the size of datasets is extremely large nowadays, parallel process- ing of complex data analysis such as DBSCAN becomes in- dispensable. However, there are three major drawbacks in the existing parallel DBSCAN algorithms. First, they fail to prop- erly balance the load among parallel tasks, especially when data are heavily skewed. Second, the scalability of these al- gorithms is limited because not all the critical sub-procedures are parallelized. Third, most of them are not primarily de- signed for shared-nothing environments, which makes them less portable to emerging parallel processing paradigms. In this paper, we present MR-DBSCAN, a scalable DBSCAN algorithm using MapReduce. In our algorithm, all the crit- ical sub-procedures are fully parallelized. As such, there is no performance bottleneck caused by sequential process- ing. Most importantly, we propose a novel data partitioning method based on computation cost estimation. The objective is to achieve desirable load balancing even in the context of heavily skewed data. Besides, We conduct our evaluation us- ing real large datasets with up to 1.2 billion points. The ex- periment results well confirm the efficiency and scalability of MR-DBSCAN.

AN INTEGRATED COUPLING ELEMENT FOR VEHICLE-RAIL-BRIDGE INTERACTION SYSTEM WITH A NON-UNIFORM CONTINUOUS BRIDGE

An integrated coupling element considering wheel-rail interface for analyzing the dynamic responses of vehicle-rail-bridge interaction system with a non-uniform continuous bridge is presented. The governing equations of the interaction system are established first, and the solution procedure and assembly method of the coupling element are demonstrated. Finally, the accuracy, efficiency and function of the integrated coupling element are tested using two numerical examples. The influences of different combinations of rail and bridge element length in the coupling element on the solution are investigated, and the effects of different rail irregularities on the dynamic responses are discussed.

Combination of heterogeneous Fenton-like reaction and photocatalysis using Co–TiO_2 nanocatalyst for activation of KHSO_5 with visible light irradiation at ambient conditions

A novel coupled system using Co–Ti O2 was successfully designed which combined two different heterogeneous advanced oxidation processes, sulfate radical based Fenton-like reaction（SR-Fenton） and visible light photocatalysis（Vis-Photo）, for degradation of organic contaminants. The synergistic effect of SR-Fenton and Vis-Photo was observed through comparative tests of 50 mg/L Rhodamine B（Rh B） degradation and TOC removal. The Rhodamine B degradation rate and TOC removal were 100% and 68.1% using the SR-Fenton/Vis-Photo combined process under ambient conditions, respectively. Moreover, based on XRD, XPS and UV-DRS characterization, it can be deduced that tricobalt tetroxide located on the surface of the catalyst is the SR-Fenton active site, and cobalt ion implanted in the Ti O2 lattice is the reason for the visible light photocatalytic activity of Co–Ti O2. Finally, the effects of the calcination temperature and cobalt concentration on the synergistic performance were also investigated and a possible mechanism for the synergistic system was proposed. This coupled system exhibited excellent catalytic stability and reusability,and almost no dissolution of Co2＋was found.