Factors Affecting Japanese HPV-Vaccination: Findings from the Semi-Structured Interviews with Adolescent Girls and Caregivers

The objective of the present study was to qualitatively assess the obstructive and facilitative factors affecting adolescent girls and their caregivers when the adolescent had received or was considering receiving the Human Papilloma Virus (HPV) vaccination. Using these data, we propose recommendations for medical and nursing staff concerned with HPV vaccination. Participants were 20 adolescent girls (aged 10 - 19 years) and their caregivers, who had visited any of the 3 pediatric clinics in the Tokyo metropolitan area during a specified period since HPV vaccination began in Japan. The girls and their caregivers were separately interviewed by 2 child and/or family nursing care specialists with a semi-structured interview. The responses were qualitatively analyzed by 2 specialists, and the obstructive and facilitative factors affecting participants’ decision to receive HPV vaccination were extracted from the responses. Among the 20 sets of participants, 7 adolescents had completed HPV vaccination, 9 were going to receive vaccination, and 4 had not received any vaccination. The obstructive/facilitative factors related to considering or receiving HPV vaccination and actual vaccination were extracted and 4 main categories of factors were identified. Facilitators toward HPV-vaccination of daughters included clear future self-image and visions, fear Cervical Cancer (CC) and desire to escape from CC, having discussion with mothers about HPV-vaccination and CC, and to have a boyfriend. Barriers toward vaccination included the mothers’ reluctance to explain the sexual matters about HPV-vaccination to their daughters and difficulty with find the appropriate clinic or hospital to HPV-vaccination. Relevant factors about vaccination included positive family attitudes toward vaccination, having family system allowing consultation and having a public financial support for vaccination for daughters. Our conceptual model adapted from the Katz, et al. conceptual framework integrated the key barriers and facilitators as factors within each of four domains. These four domains have an important link. Especially, the environmental factors and the structural and sociocultural factors domain affect the individual adolescent and the caregiver factors domain, respectively. The results of present study suggest that medical/nursing activities centered on promoting HPV vaccination in Japan should comprehensively cover CC/vaccination/sex education in an integrated fashion, while schools and public health centers should provide opportunities for caregivers and adolescents to jointly participate in awareness education on HPV vaccination.

Semi-structured Data Extraction and Schema Knowledge Mining

A semi structured data extraction method to get the useful information embedded in a group of relevant web pages and store it with OEM(Object Exchange Model) is proposed. Then, the data mining method is adopted to discover schema knowledge implicit in the semi structured data. This knowledge can make users understand the information structure on the web more deeply and thourouly. At the same time, it can also provide a kind of effective schema for the querying of web information.

A model-driven approach to semi-structured database design

Recently XML has become a standard for data representation and the preferred method of encoding struc- tured data for exchange over the Internet. Moreover it is fre- quently used as a logical format to store structured and semi- structured data in databases. We propose a model-driven and configurable approach for modeling hierarchical XML data using object role modeling （ORM） as a flat conceptual model. First a non-hierarchical conceptual schema of the problem domain is built using ORM and then different hierarchical views of the conceptual schema or parts of it are specified by the designer using transformation rules. A hierarchical mod- eling notation called H-ORM is proposed to show these hier- archical views and model more complex semi-structured data constructs and constraints. We also propose an algorithm to map hierarchical H-ORM views to XML schema language.

Fuzzy Optimum Model of Semi-Structural Decision for Lectotype Optimization of Offshore Platforms

In the process of concept design of offshore platforms, it is necessary to select the best from feasible alternatives through comparison and filter. The criterion set, used to evaluate and select the satisfying alternative, consists of many qualitative and quantitative factors. Therefore, the selection is a problem of multicriteria and semi-structural decision-making. Different from traditional methods in semi-structural decision-making, a new framework and methodology is presented in this paper for evaluation of offshore platform alternatives, First, the criterion set is established for the evaluation of alternatives. Next, the approach is studied to construct the relative membership degree matrix, in which both qualitative and quantitative factors are consistent with the uniform calculating standard. And then a new weight-assessing method is developed for calculation of the weights based on the relative membership degree matrix. Finally, a multi-hierarchy fuzzy optimum model is adopted to select the satisfying offshore platform alternative. A case study shows that the new framework and methodology are scientific, reasonable and easy to use in practice.

Logformer: Cascaded Transformer for System Log Anomaly Detection

Modern large-scale enterprise systems produce large volumes of logs that record detailed system runtime status and key events at key points.These logs are valuable for analyzing performance issues and understanding the status of the system.Anomaly detection plays an important role in service management and system maintenance,and guarantees the reliability and security of online systems.Logs are universal semi-structured data,which causes difficulties for traditional manual detection and pattern-matching algorithms.While some deep learning algorithms utilize neural networks to detect anomalies,these approaches have an over-reliance on manually designed features,resulting in the effectiveness of anomaly detection depending on the quality of the features.At the same time,the aforementioned methods ignore the underlying contextual information present in adjacent log entries.We propose a novel model called Logformer with two cascaded transformer-based heads to capture latent contextual information from adjacent log entries,and leverage pre-trained embeddings based on logs to improve the representation of the embedding space.The proposed model achieves comparable results on HDFS and BGL datasets in terms of metric accuracy,recall and F1-score.Moreover,the consistent rise in F1-score proves that the representation of the embedding spacewith pre-trained embeddings is closer to the semantic information of the log.

Handling Big Data in Relational Database Management Systems

Currently, relational database management systems (RDBMSs)face different challenges in application development due to the massive growthof unstructured and semi-structured data. This introduced new DBMS categories, known as not only structured query language (NoSQL) DBMSs, whichdo not adhere to the relational model. The migration from relational databasesto NoSQL databases is challenging due to the data complexity. This study aimsto enhance the storage performance of RDBMSs in handling a variety of data.The paper presents two approaches. The first approach proposes a convenientrepresentation of unstructured data storage. Several extensive experimentswere implemented to assess the efficiency of this approach that could resultin substantial improvements in the RDBMSs storage. The second approachproposes using the JavaScript Object Notation (JSON) format to representmultivalued attributes and many to many (M:N) relationships in relationaldatabases to create a flexible schema and store semi-structured data. Theresults indicate that the proposed approaches outperform similar approachesand improve data storage performance, which helps preserve software stabilityin huge organizations by improving existing software packages whose replacement may be highly costly.

Priority setting in health care: Attitudes of physicians and patients

Background: The opinion of physicians clearly counts in prioritizing health care, but there is little information on the rationales underlying treatment decisions and whether these rationales are accepted by patients. Objective: To compare physicians and patients regarding their understanding and use of therapeutic benefit and treatment costs as criteria for prioritizing health care. Methods: Seven physicians and twelve patients were purposefully selected to yield a heterogeneous sample. Participants were interviewed face-to-face, following a semi-structured topic guide comprising three scenarios that focused on interventions with low or unproven therapeutic benefit and high costs, respectively. For data analysis we used qualitative content analysis. Results: We found that patients and physicians differed in their understanding of therapeutic benefit, their expectations of what medicine can do and their use of costs as criteria for prioritizing health care. Physicians were less likely to assess a certain intervention as effec tive, and they less often accepted upper funding limits in health care. Unlike the physicians, patients raised non-medical aspects in decision making such as the patient’s consent and social inequalities. Conclusions: The revealed differences point toward the necessity to strengthen the doctor-patient communication, to improve information for patients about the possibilities and limits of health care and to gain a deeper understanding of their attitudes, wishes and concerns to reach an agreement by physicians and patients on the treatment to be implemented.

Systematic Review of Diabetes Self-Management: Focusing on the Middle-Aged Population of Pakistan and Saudi Arabia

The aim is to synthesize the most contemporary qualitative research on the self-management of type 2 diabetes with specific interest in the population of Pakistan and Saudi Arabia. The electronic databases searched include the Cochrane library, MEDLINE, PubMed, EMBASE and PsycINFO, between the year 1993 and 2013. The inclusion criteria was the middle-aged population aged 40 - 60 years. Studies must report qualitative research on diabetes self-management, diabetic complications, quality of life, and patient-doctor relationship or interaction. Out of the 36 identified studies, 30 studies from the literature search representing self-management in context suggest that the multiple contextual factors identified are the fertile ground for further research, and the context which is useful for health care professionals suggests that coping with diagnosis and living with diabetes are affected by a complex constellation of factors, including life circumstances, social support, gender roles and economy. Three conceptual themes were identified from the analysis. The review has revealed that there is a lack of studies in literature on self-management of type 2 diabetes in both the countries.

A Tree Pattern Matching Algorithm for XML Queries with Structural Preferences

In the XML community, exact queries allow users to specify exactly what they want to check and/or retrieve in an XML document. When they are applied to a semi-structured document or to a document with an overly complex model, the lack or the ignorance of the explicit document model (DTD—Document Type Definition, Schema, etc.) increases the risk of obtaining an empty result set when the query is too specific, or, too large result set when it is too vague (e.g. it contains wildcards such as “*”). The reason is that in both cases, users write queries according to the document model they have in mind;this can be very far from the one that can actually be extracted from the document. Opposed to exact queries, preference queries are more flexible and can be relaxed to expand the search space during their evaluations. Indeed, during their evaluation, certain constraints (the preferences they contain) can be relaxed if necessary to avoid precisely empty results;moreover, the returned answers can be filtered to retain only the best ones. This paper presents an algorithm for evaluating such queries inspired by the TreeMatch algorithm proposed by Yao et al. for exact queries. In the proposed algorithm, the best answers are obtained by using an adaptation of the Skyline operator (defined in relational databases) in the context of documents (trees) to incrementally filter into the partial solutions set, those which satisfy the maximum of preferential constraints. The only restriction imposed on documents is No-Self-Containment.

An Improved Fine-Grained Encryption Method for Unstructured Big Data

In the big data protecting technologies, most of the existing data protections adopt entire encryption that leads to the researches of lightweight encryption algorithms, without considering from the protected data itself. In our previous paper (FGEM), it finds that not all the parts of a data need protections,the entire data protection can be supplanted as long as the critical parts of the structured data are protected. Reducing unnecessary encryption makes great sense for raising efficiency in big data processing. In this paper, the improvement of FGEM makes it suitable to protect semi-structured and unstructured data efficiently. By storing semi-structured and unstructured datum in an improved tree structure, the improved FGEM for the datum is achieved by getting congener nodes. The experiments show the improved FGEM has short operating time and low memory consumption.

Chopper： Efficient Algorithm for Tree Mining

With the development of Internet, frequent pattern mining has been extendedto more complex patterns like tree mining and graph mining. Such applications arise in complexdomains like bioinformatics, web mining, etc. In this paper, we present a novel algorithm, namedChopper, to discover frequent subtrees from ordered labeled trees. An extensive performance studyshows that the newly developed algorithm outperforms TreeMiner V, one of the fastest methodsproposed previously, in mining large databases. At the end of this paper, the potential improvementof Chopper is mentioned.

A survey of uncertain data management

Uncertain data are data with uncertainty information,which exist widely in database applications.In recent years,uncertainty in data has brought challenges in almost all database management areas such as data modeling,query representation,query processing,and data mining.There is no doubt that uncertain data management has become a hot research topic in the field of data management.In this study,we explore problems in managing uncertain data,present state-of-the-art solutions,and provide future research directions in this area.The discussed uncertain data management techniques include data modeling,query processing,and data mining in uncertain data in the forms of relational,XML,graph,and stream.

Complexities of practicing architectural regionalism in India:An interview study

This paper presents the results and analysis from an interview study conducted with practitioners of architectural regionalism in India.The interviews sought to gain indepth understanding of the strategies,mechanisms,and tools they employ to realize contextualized architecture that responds to local needs and potential.A sample composed of nine eminent Indian architects who regularly integrate the ideas of critical regionalism in their designs is selected and subsequently interviewed with regard to the varied aspects of their architectural practice.Findings are useful for practitioners and scholars of contemporary architecture in India for understanding the means employed by leading regionalist architects,while placing their work in the context of local building traditions,urban landscape,sociocultural conditions,technology,and climate.