New distances for dual hesitant fuzzy sets and their application in clustering algorithm

Compared to hesitant fuzzy sets and intuitionistic fuzzy sets, dual hesitant fuzzy sets can model problems in the real world more comprehensively. Dual hesitant fuzzy sets explicitly show a set of membership degrees and a set of non-membership degrees, which also imply a set of important data: hesitant degrees.The traditional definition of distance between dual hesitant fuzzy sets only considers membership degree and non-membership degree, but hesitant degree should also be taken into account. To this end, using these three important data sets (membership degree, non-membership degree and hesitant degree), we first propose a variety of new distance measurements (the generalized normalized distance, generalized normalized Hausdorff distance and generalized normalized hybrid distance) for dual hesitant fuzzy sets in this paper, based on which the corresponding similarity measurements can be obtained. In these distance definitions, membership degree, non-membership-degree and hesitant degree are of equal importance. Second, we propose a clustering algorithm by using these distances in dual hesitant fuzzy information system. Finally, a numerical example is used to illustrate the performance and effectiveness of the clustering algorithm. Accordingly, the results of clustering in dual hesitant fuzzy information system are compared using the distance measurements mentioned in the paper, which verifies the utility and advantage of our proposed distances. Our work provides a new way to improve the performance of clustering algorithms in dual hesitant fuzzy information systems.

Content and structure of ward rounds focusing interprofessional collaboration on an internal medicine ward: An observational study of interprofessional collaboration

Ward rounds are crucial for the exchange of information among healthcare professionals to achieve joint planning and shared decision-making in healthcare to enhance patient safety. The aim of this study was to describe the content and structure of ward rounds focusing on interprofessional collaboration on an internal medicine ward at a university hospital in Western Sweden. An inductive qualitative approach was used to explore 13 participatory observations of ward rounds (sitting/team rounds). Qualitative content analysis was used. The analysis revealed one category, titled interprofessional teamwork, that utilises all available resources, which consisted of three subcategories: usefulness of specialist competencies, collaboration for patient safety, and leading healthcare to achieve goal fulfilment. It was also found that the participating specialists’ competencies were not being optimally used before patients were discharged from the hospital. Therefore, communication and leadership skills were revealed as ways to improve interprofessional teamwork to achieve goal fulfilment and patient safety regarding care and treatment issues on the ward. We found that reversing the order of ward rounds to start with the sitting round followed by the team round (i.e. hybrid distance participation methods), with the same ward round leader who has skills in leadership and interprofessional teamwork, could eliminate the need for healthcare providers to repeat questions and tasks (i.e. double work) on their ward rounds. Second, patient involvement is grounded in collaboration, and can be emphasised through person-centred care to facilitate patient safety during hospital stays.

Improved Indoor Fingerprinting Localization Method Using Clustering Algorithm and Dynamic Compensation

Many indoor fingerprinting localization methods are based on signal-domain distances with large localization error and low stability. An improved fingerprinting localization method using a clustering algorithm and dynamic compensation was proposed. In the offline stage, the fingerprint database was built and clustered based on offline hybrid distance and an affinity propagation clustering algorithm. Furthermore, clusters were adjusted using transition regions and a given radius, as well as updating the corresponding position and fingerprint of the cluster centroid. In the online stage, the lost received signal strength (RSS) in the reference fingerprint would be dynamically compensated by using a minimum RSS value, rather than a fixed one. Online signal-domain distance was calculated for cluster identification based on RSS readings and compensated reference fingerprint. Then, K reference points with minimum online signal-domain distances were selected, and affinity propagation clustering was reused by position-domain distances to choose the position-concentrated sub-cluster for location estimation. Experimental results show that the proposed method outperforms state-of-the-art fingerprinting methods, with the mean error of 2.328 m, the root mean square error of 1.865 m and the maximum error of 10.722 m in a testbed of 3200 square meters. The improvement rates, in terms of accuracy and stability, are more than 21% and 13%, respectively.

Digital Humanities and Virtual Reality: A Review of Theories and Best Practices for Art History

The technology of virtual reality (VR) has had proven educational benefits over the last three decades. Yet, most research conducted on these benefits has been confined to the sciences, especially in Computer Science. The application of VR technology for the Digital Humanities is only now beginning to receive attention, but more study needs to be conducted on its uses within various humanistic disciplines. In order to expand on the research at a pivotal time in education when modalities expand to incorporate more hybrid, distance education, and online learning, this study reviews the literature and theories behind the educational use of VR as a tool to reinforce learning outcomes in the field of art history. Through the review, theories and best practices are presented for practical adoption at various level of instruction at other institutions, as well as a list of recommendations for integrating VR technology specifically within art history instruction.

Evolution of hybrid distance based kNN classification

<span id="docs-internal-guid-b63d466d-7fff-f94f-7540-9cb92d7bb505"><span>The evolution of classification of opinion mining and user review analysis span from decades reaching into ubiquitous computing in efforts such as movie review analysis. The performance of linear and non-linear models are discussed to classify the positive and negative reviews of movie data sets. The effectiveness of linear and non-linear algorithms are tested and compared in-terms of average accuracy. The performance of various algorithms is tested by implementing them on internet movie data base (IMDB). The hybrid kNN model optimizes the performance classification interns of accuracy. The accuracy of polarity prediction rate is improved with random-distance-weighted-kNN-ABC when compared with kNN algorithm applied alone.</span></span>

Applying the Super-EBM Model and Spatial Durbin Model to Examining Total Factor Ecological Efficiency From a Multi-Dimensional Perspective: Evidence From China

Ecological efficiency mainly emphasizes the importance of balancing the relationship between natural resources,energy,ecological environment and economic growth, which has aroused widespread concern in the world.China's rapid economic development has inevitably accompanied by serious resource exhaustion,environmental pollution and ecological deterioration in the past several decades,which has brought huge challenges to China's sustainable development.Therefore, establishing the evaluation framework of total-factor ecological efficiency (TFEE) and identifying its driving force has great significance for improving China's sustainable development capabilities.Firstly, a ecological efficiency evaluation framework is established based on the theory of total factor analysis.Secondly,establishing the Super-efficient hybrid distance model consider undesirable output,and measuring the total-factor ecological efficiency of nationwide,30 provinces and four regions during the period 2003–2017.Finally, the spatial effect of total-factor ecological efficiency and its driving factor are examined by using a Spatial Durbin model. The empirical results show that: (1)The efficiency measurement results show that the TFEE of China overall and regional showed different degrees of decline during the study period.There are significant differences among 30 provinces and four regions.Beijing,Tianjin,Shanghai are efficient,and the other provinces has not been effective.The TFEE of four region's are not achieve effective,and shows the distribution pattern of the eastern > northeast > central > western .(2)Moran’s I index show that the TFEE in nationwide has a positive spatial autocorrelation,and showing a strong spatial agglomeration.However,the spatial distribution pattern of TFEE in China was unstable and easy to change;Moran scatter plot indicates that china's provincial TFEE has not only spatial dependence characteristics, but also spatial differences in spatial correlation.(3)Most factors are bound up with TFEE in various degree, in which, TP,JJ and HC play a positive in TFEE ,and IS,CITY, and EI play a negative role in TFEE. Furthermore,ER show U type of relationship with TFEE.GDP and FDI cannot have a significant impact on TFEE at this stage.(4)The spatial Durbin model results show that TFEE has significant spatial spillover effect, and the improvement of the TFEE of province will increase the this TFEE of neighboring provinces.And spatial spillover effects of TP,IS,JJ,CITY,and HC are confirmed can significant impact the improvement of TFEE in neighboring provinces.

Kelantan and Sarawak Malay Dialects: Parallel Dialect Text Collection and Alignment Using Hybrid Distance-Statistical-Based Phrase Alignment Algorithm

Parallel texts corpora are essential resources especially in translation and multilingual information retrieval. However, the publicly available parallel text corpora are limited to certain types and domains. Besides, Malay dialects are not standardized in term of writing. The existing alignment algorithms that is used to analayze the writing will require a large training data to obtain a good result. The paper describes our methodology in acquiring a parallel text corpus of Standard Malay and Malay dialects, particularly Kelantan Malay and Sarawak Malay. Second, we propose a hybrid of distance-based and statistical-based alignment algorithm to align words and phrases of the parallel text. The proposed approach has a better precision and recall than the state-of-the-art GIZA++. In the paper, the alignment obtained were also compared to find out the lexical similarities and differences between SM and the two dialects.