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This paper explores the political and legal issues contained in the law and jurisprudence surrounding missing American service personnel. It argues that the Missing Service Personnel Act of 1995 is an effect of the legacy of the Vietnam War rather than a response to a particular legal problem. The essay further contends that we should be suspicious of the effort to transform the balance sheet of war into a justiciable legal question, primarily because the requirement to produce a body fails to disarm the representational economy in which the absent body constitutes a continuation of Vietnam War hostilities.

This chapter outlines how the comprehensive North American and European datasets were collected and explains the ensuing data cleaning process outlining three alternative methods applied to deal with missing values, the complete case, the multiple imputation (MI), and the K-nearest neighbor (KNN) methods. The complete case method is the conventional approach adopted in many mainstream management studies. We further discuss the implied assumption underlying use of this technique, which is rarely assessed, or tested in practice and explain the alternative imputation approaches in detail. Use of North American data is the norm but we also collected a European dataset, which is rarely done to enable subsequent comparative analysis between these geographical regions. We introduce the structure of firms organized within different industry classification schemes for use in the ensuing comparative analyses and provide base information on missing values in the original and cleaned datasets. The calculated performance indicators derived from the sampled data are defined and presented. We show how the three alternative approaches considered to deal with missing values have significantly different effects on the calculated performance measures in terms of extreme estimate ranges and mean performance values.

This paper sets a case study of missing children in the Republic of Ireland against a review of international research to explore broader understandings and responses to the problem.

The study begins by reviewing the literature on pioneering American initiatives dating back to the 1970s and more recent literature from Great Britain where a series of high-profile scandals involving sexual exploitation of teenage girls provoked a number of controversial inquiries into the police and social work professions. The present study was prompted by an evaluation of the 116 000 Missing Children Hotline which was introduced to Ireland in 2012 under the auspices of the European Union (EU) Daphne III Programme by the Irish Society for the Prevention of Cruelty to Children (ISPCC).

The central conclusion emerging from analysis of the evidence is that Missing Children Hotlines remain rooted in representations of ‘stranger danger’ and disconnected from repeat runaway children who feature prominently in police reports from formal care settings or family homes and who are actively targeted by sexual predators and criminal gangs. The implications are that systemic change requires grounding in research strategies which combine police data with anthropological studies to give legitimacy to the voices of runway and sexually exploited children.

The study offers original international perspectives on missing children to epistemological research communities in the fields of social work, criminology and policing with recommendations that Missing Children and Runaway Safe-lines are targeted systemically at keeping runaway children, homeless children and at-risk-youth safe and off the streets.

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.

Missing data are a pervasive problem in special education research. The purpose of this chapter is to provide researchers with an overview of two “modern” alternatives for handling missing data, full information maximum likelihood (FIML) and multiple imputation (MI). These techniques are currently considered to be the methodological “state of the art”, and generally provide more accurate parameter estimates than the traditional methods that are still common in published educational studies. The chapter begins with an overview of missing data theory, and provides brief descriptions of some traditional missing data techniques and their requisite assumptions. Detailed descriptions of FIML and MI are given, and the chapter concludes with an analytic example from a longitudinal study of depression.

Numerous studies have looked at why people attend events which engage in conspicuous consumerism, but they have neglected the fear of missing out on these event-based experiences. This study aims to look at the impact of sensation seeking on conspicuous consumption within the event-based activities. Moreover, the developed model examined the mediating role of the fear of missing out in this impact.

A questionnaire survey was conducted, and a conceptual framework was performed to test hypothesized links between the three variables.

The findings show that sensation seeking affects conspicuous consumption, and fear of missing out has a mediating effect on this relationship.

The results of the study give some theoretical and practical implications to practitioners and researchers about aspirational class as elite consumers and high-level attendees of one-off events.

The purpose of this paper is to give an account for preparative collaboration between the police and municipal eldercare in Sweden concerning missing persons with dementia.

Design/methodology/approach used was a qualitative case study design, consisting of one focus group with representatives for the participating organisations, followed by directed content analysis.

The findings showed a lack of current collaboration and reluctance to new collaborative initiatives. However, when focussing on preparative collaboration of coordinated responses to missing incidents, possibilities for improvement could be identified. The improvements concerned updated personnel response checklists, along with suggestions for an elaborate life story document in eldercare, with police access. Finally, better coordination of the return of the found person and a follow up were proposed.

It is suggested that collaboration must be given different meanings and use different approaches adapted to the different phases in a rescue operation. In the preparation and the response phase, the focus should be on coordination of the resources available. In the pre-planning and prevention phases, as well as in evaluation and learning, horizontal collaboration is more suitable. The amount of data in this study is a research limitation which calls for further research.

It is suggested that collaboration must be given different meanings and use different approaches adapted to the different phases in a rescue operation. In the preparation and the response phase, the focus should be on coordination of the resources available. In the pre-planning and prevention phases, as well as in evaluation and learning, horizontal co-operation is more suitable.

The originality/value of this paper can be found in the novelty of missing person research in Sweden, and in practical suggestions for preparative collaboration concerning persons with dementia who go missing. Finally, it can be found in the suggested need for a more dynamic and process-sensitive view of collaboration in SAR or rescue operations.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

The missing travel time data for roads is a common problem encountered by traffic management departments. Tensor decomposition, as one of the most widely used method for completing missing traffic data, plays a significant role in the intelligent transportation system (ITS). However, existing methods of tensor decomposition focus on the global data structure, resulting in relatively low accuracy in fibrosis missing scenarios. Therefore, this paper aims to propose a novel tensor decomposition model which further considers the local spatiotemporal similarity for fibrosis missing to improve travel time completion accuracy.

The proposed model can aggregate road sections with similar physical attributes by spatial clustering, and then it calculates the temporal association of road sections by the dynamic longest common subsequence. A similarity relationship matrix in the temporal dimension is constructed and incorporated into the tensor completion model, which can enhance the local spatiotemporal relationship of the missing parts of the fibrosis type.

The experiment shows that this method is superior and robust. Compared with other baseline models, this method has the smallest error and maintains good completion results despite high missing rates.

This model has higher accuracy for the fibrosis missing and performs good convergence effects in the case of the high missing rate.