Qiong Wu, Zhiwei Zeng, Jun Lin and Yiqiang Chen
Poor medication adherence leads to high hospital admission rate and heavy amount of health-care cost. To cope with this problem, various electronic pillboxes have been proposed to…
Abstract
Purpose
Poor medication adherence leads to high hospital admission rate and heavy amount of health-care cost. To cope with this problem, various electronic pillboxes have been proposed to improve the medication adherence rate. However, most of the existing electronic pillboxes use time-based reminders which may often lead to ineffective reminding if the reminders are triggered at inopportune moments, e.g. user is sleeping or eating.
Design/methodology/approach
In this paper, the authors propose an AI-empowered context-aware smart pillbox system. The pillbox system collects real-time sensor data from a smart home environment and analyzes the user’s contextual information through a computational abstract argumentation-based activity classifier.
Findings
Based on user’s different contextual states, the smart pillbox will generate reminders at appropriate time and on appropriate devices.
Originality/value
This paper presents a novel context-aware smart pillbox system that uses argumentation-based activity recognition and reminder generation.
Details
Keywords
Yuqin Wang, Bing Liang, Wen Ji, Shiwei Wang and Yiqiang Chen
In the past few years, millions of people started to acquire knowledge from the Massive Open Online Courses (MOOCs). MOOCs contain massive video courses produced by instructors…
Abstract
Purpose
In the past few years, millions of people started to acquire knowledge from the Massive Open Online Courses (MOOCs). MOOCs contain massive video courses produced by instructors, and learners all over the world can get access to these courses via the internet. However, faced with massive courses, learners often waste much time finding courses they like. This paper aims to explore the problem that how to make accurate personalized recommendations for MOOC users.
Design/methodology/approach
This paper proposes a multi-attribute weight algorithm based on collaborative filtering (CF) to select a recommendation set of courses for target MOOC users.
Findings
The recall of the proposed algorithm in this paper is higher than both the traditional CF and a CF-based algorithm – uncertain neighbors’ collaborative filtering recommendation algorithm. The higher the recall is, the more accurate the recommendation result is.
Originality/value
This paper reflects the target users’ preferences for the first time by calculating separately the weight of the attributes and the weight of attribute values of the courses.
Details
Keywords
Jing Liu, Zhiwen Pan, Jingce Xu, Bing Liang, Yiqiang Chen and Wen Ji
With the development of machine learning techniques, the artificial intelligence systems such as crowd networks are becoming more autonomous and smart. Therefore, there is a…
Abstract
Purpose
With the development of machine learning techniques, the artificial intelligence systems such as crowd networks are becoming more autonomous and smart. Therefore, there is a growing demand for developing a universal intelligence measurement so that the intelligence of artificial intelligence systems can be evaluated. This paper aims to propose a more formalized and accurate machine intelligence measurement method.
Design/methodology/approach
This paper proposes a quality–time–complexity universal intelligence measurement method to measure the intelligence of agents.
Findings
By observing the interaction process between the agent and the environment, we abstract three major factors for intelligence measure as quality, time and complexity of environment.
Originality/value
This paper proposes a calculable universal intelligent measure method through considering more than two factors and the correlations between factors which are involved in an intelligent measurement.
Details
Keywords
Zhiwen Pan, Jiangtian Li, Yiqiang Chen, Jesus Pacheco, Lianjun Dai and Jun Zhang
The General Society Survey(GSS) is a kind of government-funded survey which aims at examining the Socio-economic status, quality of life, and structure of contemporary society…
Abstract
Purpose
The General Society Survey(GSS) is a kind of government-funded survey which aims at examining the Socio-economic status, quality of life, and structure of contemporary society. GSS data set is regarded as one of the authoritative source for the government and organization practitioners to make data-driven policies. The previous analytic approaches for GSS data set are designed by combining expert knowledges and simple statistics. By utilizing the emerging data mining algorithms, we proposed a comprehensive data management and data mining approach for GSS data sets.
Design/methodology/approach
The approach are designed to be operated in a two-phase manner: a data management phase which can improve the quality of GSS data by performing attribute pre-processing and filter-based attribute selection; a data mining phase which can extract hidden knowledge from the data set by performing data mining analysis including prediction analysis, classification analysis, association analysis and clustering analysis.
Findings
According to experimental evaluation results, the paper have the following findings: Performing attribute selection on GSS data set can increase the performance of both classification analysis and clustering analysis; all the data mining analysis can effectively extract hidden knowledge from the GSS data set; the knowledge generated by different data mining analysis can somehow cross-validate each other.
Originality/value
By leveraging the power of data mining techniques, the proposed approach can explore knowledge in a fine-grained manner with minimum human interference. Experiments on Chinese General Social Survey data set are conducted at the end to evaluate the performance of our approach.
Details
Keywords
Zhiwen Pan, Wen Ji, Yiqiang Chen, Lianjun Dai and Jun Zhang
The disability datasets are the datasets that contain the information of disabled populations. By analyzing these datasets, professionals who work with disabled populations can…
Abstract
Purpose
The disability datasets are the datasets that contain the information of disabled populations. By analyzing these datasets, professionals who work with disabled populations can have a better understanding of the inherent characteristics of the disabled populations, so that working plans and policies, which can effectively help the disabled populations, can be made accordingly.
Design/methodology/approach
In this paper, the authors proposed a big data management and analytic approach for disability datasets.
Findings
By using a set of data mining algorithms, the proposed approach can provide the following services. The data management scheme in the approach can improve the quality of disability data by estimating miss attribute values and detecting anomaly and low-quality data instances. The data mining scheme in the approach can explore useful patterns which reflect the correlation, association and interactional between the disability data attributes. Experiments based on real-world dataset are conducted at the end to prove the effectiveness of the approach.
Originality/value
The proposed approach can enable data-driven decision-making for professionals who work with disabled populations.
Details
Keywords
The publishers of International Journal of Crowd Science wish to retract the article “Quality-time-complexity universal intelligence measurement”, by Wen Ji, Jing Liu, Zhiwen Pan…
Abstract
The publishers of International Journal of Crowd Science wish to retract the article “Quality-time-complexity universal intelligence measurement”, by Wen Ji, Jing Liu, Zhiwen Pan, Jingce Xu, Bing Liang, and Yiqiang Chen, which appeared in volume 2, issue 1, 2018. It has come to our attention that due to an error in the submission process the above article is an earlier version of the article published in International Journal of Crowd Science, volume 2, issue 2, 2018 (DOI: https://doi.org/10.1108/IJCS-04-2018-0007). The duplicate publication was the result of an inadvertent administrative error by the authors. The authors and publishers of the journal sincerely apologise to the readers.
Yiqiang Zhou and Lianghua Chen
This study aims to investigate whether public attention influences corporate decisions on environmental disclosure, thereby revealing how society perceives and understands…
Abstract
Purpose
This study aims to investigate whether public attention influences corporate decisions on environmental disclosure, thereby revealing how society perceives and understands environmental issues and how corporations respond to these expectations.
Design/methodology/approach
We selected publicly listed Chinese firms as our sample. An “Environmental Disclosure Greenwashing” (EDG) Index was developed through textual analysis of their annual reports using natural language processing. Financial data were obtained from the CSMAR database, and multivariate regression was used for analysis.
Findings
The impact of public attention on EDG primarily manifests as an oversight pressure effect rather than a legitimacy incentive effect. As public attention intensifies, firms tend to adopt more substantial environmental actions instead of merely symbolic environmental disclosures. Formal regulatory frameworks might inadvertently trigger corporate EDG, but public attention can correct the adverse effects possibly introduced by formal regulations. Notably, in firms facing lower institutional pressure, the influence of public attention is more pronounced.
Practical implications
The evidence suggests that public attention reduces corporate EDG. These findings have significant implications for the regulation of environmental disclosures among firms in emerging economies.
Originality/value
The study integrates research in environmental disclosure with the concept of “greenwashing”, unveiling the limitations of the “disclosure as governance” viewpoint. It elucidates the impact of an informal external oversight mechanism (i.e. public attention) on complex corporate environmental disclosure decisions.
Details
Keywords
Wenchao Duan, Yiqiang Yang, Wenhong Liu, Zhiqiang Zhang and Jianzhong Cui
The purpose of this paper is to reveal the solute segregation behavior in the molten and solidified regions during direct chill (DC) casting of a large-size magnesium alloy slab…
Abstract
Purpose
The purpose of this paper is to reveal the solute segregation behavior in the molten and solidified regions during direct chill (DC) casting of a large-size magnesium alloy slab under no magnetic field (NMF), harmonic magnetic field (HMF), pulsed magnetic field (PMF) and two types of out-of-phase pulsed magnetic field (OPMF).
Design/methodology/approach
A 3-D multiphysical coupling mathematical model is used to evaluate the corresponding physical fields. The coupling issue is addressed using the method of separating step and result inheritance.
Findings
The results suggest that the solute deficiency tends to occur in the central part, while the solute-enriched area appears near the fillet in the molten and solidified regions. Applying magnetic field could greatly homogenize the solute field in the two-phase region. The variance of relative segregation level in the solidified cross-section under NMF is 38.1%, while it is 21.9%, 18.6%, 16.4% and 12.4% under OPMF2 (the current phase in the upper coil is ahead of the lower coil), HMF, PMF and OPMF1 (the current phase in the upper coil lags behind the lower coil), respectively, indicating that OPMF1 is more effective to reduce the macrosegregation level.
Originality/value
There are few reports on the solute segregation degree in rectangle slab under magnetic field, especially for magnesium alloy slab. This paper can act a reference to make clear the solute transport behavior and help reduce the macrosegregation level during DC casting.
Details
Keywords
Yiqiang Wang, Zhengcai Guo, Botao Liu, Yanfei Zhu and Haibo Luo
The alignment precision of existing methods is limited by the precision of detecting element and worker’s experience, which the parallelism between ball screw and guide way is not…
Abstract
Purpose
The alignment precision of existing methods is limited by the precision of detecting element and worker’s experience, which the parallelism between ball screw and guide way is not guaranteed effectively. Thus, this paper aims to propose a method of detecting ball screw’s alignment error (BSAE) via monitoring the average vibration magnitude induced by rotational frequency of ball screw (VMRFBS).
Design/methodology/approach
In this study, the ball screw is simplified as a freely supported beam. A mathematical model of the effect of BSAE on the contact angle of the ball and screw is established. The change of contact angle has effect on the deformation and contact stiffness according to the Hertz contact theory. To improve the accuracy of the experimental results, the VMRFBS are analyzed by using average method, and the average values of the VMRFBS at different BSAEs are calculated by using the least squares method.
Findings
The experimental results show that the average VMRFBS increases with the increasing of BSAE under the BSAE from 0 to 0.2 mm, while the other conditions are unchanged.
Originality/value
This method provides an approach to monitor the BSAE and improve the alignment accuracy of machine tools and automation equipment, which has a certain guide for improving the alignment accuracy of ball screw.
Details
Keywords
The purpose of this paper is to investigate and analyze the efficiency and stability of the implementation of the Crout version of ILU (ILUC) preconditioning on fast‐multipole…
Abstract
Purpose
The purpose of this paper is to investigate and analyze the efficiency and stability of the implementation of the Crout version of ILU (ILUC) preconditioning on fast‐multipole method (FMM) for solving large‐scale dense complex linear systems arising from electromagnetic open perfect electrical conductor (PEC).
Design/methodology/approach
The FMM is employed to reduce the computational complexity of the matrix‐vector product and the memory requirement of the impedance matrix. The numerical examples are initially solved by the quasi‐minimal residual (QMR) method with ILUC preconditioning. In order to fully investigate the performance of ILUC in connection with other iterative solvers, a case is also solved by bi‐conjugate gradient solver and conjugate gradient squared solver with ILUC preconditioning.
Findings
The solutions show that the ILUC preconditioner is stable and significantly improves the performance of the QMR solver on large ill‐conditioned open PEC problems compared to using ILU(0) and threshold‐based ILU (ILUT) preconditioners. It dramatically decreases the number of iterations required for convergence and consequently reduces the total CPU solving time with a reasonable overhead in memory.
Practical implications
The preconditioning scheme can be applied to large ill‐conditioned open PEC problems to effectively speed up the overall electromagnetic simulation progress while maintaining the computational complexity of FMM. More complex structures including wire‐PEC junctions and microstrip arrays may be addressed in future work.
Originality/value
The performance of ILUC has been previously reported only on preconditioning sparse linear systems, in which the ILU preconditioner is constructed by the ILUC of the coefficient matrix (e.g. matrix arised from two‐dimensional finite element convection‐diffusion problem) and subsequently applied to the same sparse linear systems; so it is important to report its performance on the dense complex linear systems that arised from open PEC electromagnetic problems. In contrast, the preconditioner is constructed upon the near‐field matrix of the FMM and subsequently applied to the whole dense linear system. The comparison of its performance against the diagonal, ILU(0) and ILUT precoditioners is also presented.