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The problem of missing children draws much attention of both governmental and nongovernmental organizations in China due to huge numbers of the missing children. According to the records of Baby Back Home network (BBHNet), a professional website to search missing children in China, 1,666 missing children have been found via releasing information on BBHNet; however, there are still 30,561 families searching for their children and 24,603 missing children are searching for biological parents through this website and have not succeeded yet. What is the difference between successful and unsuccessful cases in the aspect of released information? Motivated by this question, our research proposes to determine the crucial information in the process of searching missing children. A logistic regression model was developed on the data summarized from 500 succeed cases and 500 cases which have not succeed yet from BBHNet for forecasting success rate of searching missing children. The model identifies that the differences in terms of released information, number of children, address, and natural geographical features are the three most crucial factors that cause differences in the result of searching. This research can be used as a guide for improving the success rate of searching missing children and provide reference for developing a missing children information-sharing platform.

This paper aims to examine how consumers perceive incomplete information in print apparel advertisements in magazines and whether incomplete information influences decisions.

A total of 239 examples of print apparel advertisements in fashion magazines were collected in 2006. Content analysis was utilised to code the types of information in print apparel advertisements prior to the design of the questionnaire in the consumer survey. In the questionnaire, attitudes toward incomplete information in print apparel advertisements, information search, involvement, purchasing intentions, and demographics were included. A consumer survey was administered in Taiwan using stratified sampling. The total number of completed, usable questionnaires returned was 304.

Consumers who thought missing information in print apparel advertising to be important tended to find missing information from other sources like media, word‐of‐mouth, salespersons, and in stores. Information search behaviour positively influenced purchasing intentions. Consumers with higher levels of involvement tended to pay more attention to missing information and were more likely to search information.

Print apparel advertisements are presented not only in magazines, but on outdoor billboards, in catalogues, on the internet, in newspapers, and in buses. The restrictions of readers of fashion magazines as respondents in the study could limit the applicability of research findings of the study to attitudes toward incomplete information in print apparel advertisements of fashion magazine readers.

Print apparel advertisements are not a major source for consumers to obtain comprehensive fashion information. Simplified but clear design of print apparel advertising is acceptable for consumers who are prone to ignore missing information. Those who tend to notice missing information in print apparel advertisements would investigate other sources to obtain information for purchase decisions. Print apparel advertisements showing fashion clothing and brand names only are easy for browsing. The attractiveness of print apparel advertising design seems to be more important than detailed information included in advertisements.

The contribution of the study is to reveal attitudes toward incomplete information in print apparel advertising. The results of the study could be beneficial for apparel advertisers and could be valuable for marketers to realise the types of information consumers prefer while searching though the medium of fashion magazines.

This paper aims to examine our current knowledge of how searchers perceive and react to the possibility of missing potentially important information whilst searching the web is limited. The study reported here seeks to investigate such perceptions and reactions, and to explore the extent to which Simon's “bounded rationality” theory is useful in illuminating these issues.

Totally 37 academic staff, research staff and research students in three university departments were interviewed about their web searching. The open‐ended, semi‐structured interviews were inductively analysed. Emergence of the concept of “good enough” searching prompted a further analysis to explore the extent to which the data could be interpreted in terms of Simon's concepts of “bounded rationality” and “satisficing”.

The results indicate that the risk of missing potentially important information was a matter of concern to the interviewees. Their estimations of the likely extent and importance of missed information affected decisions by individuals as to when to stop searching – decisions based on very different criteria, which map well onto Simon's concepts. On the basis of the interview data, the authors propose tentative categorizations of perceptions of the risk of missing information including “inconsequential” “tolerable” “damaging” and “disastrous” and search strategies including “perfunctory” “minimalist” “nervous” and “extensive”. It is concluded that there is at least a prima facie case for bounded rationality and satisficing being considered as potentially useful concepts in our quest better to understand aspects of human information behaviour.

Although the findings are based on a relatively small sample and an exploratory qualitative analysis, it is argued that the study raises a number of interesting questions, and has implications for both the development of theory and practice in the areas of web searching and information literacy.

The paper focuses on an aspect of web searching which has not to date been well explored. Whilst research has done much to illuminate searchers' perceptions of what they find on the web, we know relatively little of their perceptions of, and reactions to information that they fail to find. The study reported here provides some tentative models, based on empirical evidence, of these phenomena.

In real-world decision-making, high accuracy data analysis is essential in a ubiquitous environment. However, we encounter missing data while collecting user-related data information because of various privacy concerns on account of a user. This paper aims to deal with incomplete data for fuzzy model identification, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features.

In this work, authors proposed a three-fold approach for fuzzy model identification in which imputation-based linear interpolation technique is used to estimate missing features of the data, and then fuzzy c-means clustering is used for determining optimal number of rules and for the determination of parameters of membership functions of the fuzzy model. Finally, the optimization of the all antecedent and consequent parameters along with the width of the antecedent (Gaussian) membership function is done by gradient descent algorithm based on the minimization of root mean square error.

The proposed method is tested on two well-known simulation examples as well as on a real data set, and the performance is compared with some traditional methods. The result analysis and statistical analysis show that the proposed model has achieved a considerable improvement in accuracy in the presence of varying degree of data incompleteness.

The proposed method works well for fuzzy model identification method, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features with varying degree of missing data as compared to some well-known methods.

Distributed data streams are an important topic of current research. In such a setting, data values will be missed, e.g. due to network errors. This paper aims to allow this incompleteness to be detected and overcome with either the user not being affected or the effects of the incompleteness being reported to the user.

A model for representing the incomplete information has been developed that captures the information that is known about the missing data. Techniques for query answering involving certain and possible answer sets have been extended so that queries over incomplete data stream histories can be answered.

It is possible to detect when a distributed data stream is missing one or more values. When such data values are missing there will be some information that is known about the data and this is stored in an appropriate format. Even when the available data are incomplete, it is possible in some circumstances to answer a query completely. When this is not possible, additional meta‐data can be returned to inform the user of the effects of the incompleteness.

The techniques and models proposed in this paper have only been partially implemented.

The proposed system is general and can be applied wherever there is a need to query the history of distributed data streams. The work in this paper enables the system to answer queries when there are missing values in the data.

This paper presents a general model of how to detect, represent, and answer historical queries over incomplete distributed data streams.

The purpose of this paper is to quantify the risk of fatality for men who are reported missing following a night out. Additionally, the paper aimed to develop search heuristics to inform the development of search strategies, through an examination of the key geographical points involved in these cases.

Cases were identified, and data collected, from online media sources supplemented with a request to UK police forces and a search of the UK Missing Persons Unit database. In total, 96 cases which occurred over a five-and-a-half-year period in the UK were included. The study compares the profile of fatalities that result from disappearances occurring in different types of geographical area. Location data were georeferenced allowing Euclidean distances between geographical locations to be generated.

In total, 60 per cent of disappearances lasting longer than 48 h resulted in fatality, rising to almost all cases after three days missing. In 89 per cent of cases bodies are recovered from water; 11 per cent on land after the individual died from a fall, hypothermia or a drugs overdose.

Search strategies can be informed by a consideration of the type of area the person was socialising (high night-time economy through to rural areas) and the geography of subsequent sightings.

In focusing on the specific circumstances of a disappearance rather than an individual’s personal characteristics, the paper offers an innovative approach to understanding risk (i.e. what is the likelihood of a particular outcome occurring) and the development of heuristics for search strategies in missing person cases.

This paper aims to automatically obtain the implant parameter from the CBCT images to improve the outcome of implant planning.

This paper proposes automatic simulated dental implant positioning on CBCT images, which can significantly improve the efficiency of implant planning. The authors introduce the fusion point calculation method for the missing tooth's long axis and root axis based on the dental arch line used to obtain the optimal fusion position. In addition, the authors proposed a semi-interactive visualization method of implant parameters that be automatically simulated by the authors' method. If the plan does not meet the doctor's requirements, the final implant plan can be fine-tuned to achieve the optimal effect.

A series of experimental results show that the method proposed in this paper greatly improves the feasibility and accuracy of the implant planning scheme, and the visualization method of planting parameters improves the planning efficiency and the friendliness of system use.

The proposed method can be applied to dental implant planning software to improve the communication efficiency between doctors, patients and technicians.

The purpose of this paper is to determine the significant factors that cause delays in surgery operations in a hospital and factors that affect productivity of surgery clinics.

Data are collected from surgery clinics of an international hospital to identify main factors that affect delays in surgery operation. A design of experiment is then performed and the significance of the effects of these factors is determined. A regression model was also developed to find a relation between surgery delays and the selected factors.

The results show that such factors as the type of doctors and missing information significantly affect the delay in surgery operations as compared with some other factors, such as admission time considered in the study.

The models developed and the results obtained in this paper are specific to the application presented. However, the procedures are general and can be applied to similar problems.

The results of the study can be used to control important factors to reduce the delays in surgery operations, reduce risks in patient health related to these delays, reduce hospital costs and improve the productivity of these clinics.

The paper considers a management problem related to surgery delays which has not been considered before and presents a procedure to study the causes of the problem. It provides a methodology that could be used to study similar problems in health care management systems and to improve these systems by analyzing operations and activities with respect to the factors that affect productivity and efficiency of such operations.

Family health can be studied using the 1994–1995 National Health Interview Survey Disability Supplement by linking children to their mothers and other family members. However, the data item required to link is missing for 13% of children. We found that unlinked children and their probable mothers differed in many respects from their counterparts who could be linked, and exclusion of these mothers and their children from the analysis could bias results by introducing error due to incomplete coverage of the target population. We developed and validated a simple algorithm to match these children with their probable mother.

International literature on missing persons suggests that a significant volume of missing person cases originate from hospitals and mental health units, resulting in considerable costs and resource demands on both police and health sectors (e.g., Bartholomew et al., 2009; Sowerby and Thomas, 2017). In the Canadian context, however, very little is known about patients reported missing from these locations – a knowledge deficit with profound implications in terms of identifying and addressing risk factors that contribute to this phenomenon. The present study is one such preliminary attempt to try to fill a significant research and policy gap.

The authors draw on data from a sample of 8,261 closed missing person reports from a Canadian municipal police service over a five-year period (2013–2018). Using multiple logistic regression, the authors identify, among other factors, who is most likely to be reported missing from these locations.

Results reveal that several factors, such as mental disabilities, senility, mental illness and addiction, are significantly related to this phenomenon. In light of these findings, the authors suggest that there is a need to develop comprehensive strategies and policies involving several stakeholders, such as health care and social service organizations, as well as the police.

Each year, thousands of people go missing in Canada with a large number being reported from hospitals and mental health units, which can be burdensome for the police and health sectors in terms of human and financial resource allocation. Yet, very little is known about patients reported missing from health services – a knowledge deficit with profound implications in terms of identifying and addressing risk factors that contribute to this phenomenon. This manuscript seeks to remedy this gap in Canadian missing persons literature by exploring who goes missing from hospitals and mental health units.