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This paper aims to evaluate the socio-economic factors of inequalities in common mental disorders (MDs) between advantaged and disadvantaged groups and also to determine the main contributors of inequality.

This cross-sectional study was conducted in 2016–2017. The authors included 763 persons by stratified cluster sampling; clusters were cities, geographical area and households. Blinder-Oaxaca decomposition technique was used to estimate of main inequalities determinant between advantaged and disadvantaged groups.

Overall prevalence of MDs was 22.6 and 35.6% in the advantage and disadvantaged groups, respectively. The concentration index was −0.013 [95% Confidence Interval (95% CI): −0.022, −0.004]; therefore, MDs were more concentrated in the deprived group. The risk of MDs in deprived group and females was 81 and 60% higher than advantaged group (OR: 1.81; 95% CI: 1.28, 2.57) and males (OR: 1.60; 95% CI: 1.21, 2.24), respectively. Educational status [−0.06 (95% CI: −0.10, −0.01)] was the highest level of contribution in inequality in gaps between groups.

The socio-demographic inequality in MDs among adult population was more explained by lower educational level, married persons and unemployment variables.

This chapter addresses one aspect of the broad issue of the psychological foundations of the dimensions of multidimensional scaling (MDS) solutions. Using empirical data from three independent studies, it is shown that the dimensionality of MDS solutions is negatively related to individual differences in the level of cognitive differentiation and integrative complexity of individuals and positively related to the individual's ability to discriminate within dimensions. MDS dimensionality is also shown to be affected by a variety of task-related variables such as perceived task difficulty, consistency in providing similarity judgments, confidence, familiarity, and importance attached to the stimuli. The chapter concludes by raising the issue of whether MDS can be validly used to describe complex cognitive processes.

Wireless network localization technology is very popular in recent years and has attracted worldwide attention. The purpose of this paper is to improve the localization accuracy of ultra-wideband (UWB) with lower localization error taking into consideration the special real environment with the closed long and narrow space.

The principle of multidimensional scaling (MDS), particle swarm optimization (PSO) and Taylor series expansion algorithm (Taylor-D) were introduced. A novel positioning algorithm, MDS-PSO-Taylor was proposed to minimize the localization error. MDS-PSO algorithm provided a more accurate preliminary coordinate by applying the PSO algorithm so that the Taylor-D was used for further enhancing the localization accuracy.

Experimental results manifested that the proposed algorithm, providing small localization error value and higher positioning accuracy, can effectively reduce errors and achieve better performance in terms of the considerable improvement of localization accuracy.

The presented study with the real environment test attempts to demonstrate the proposed algorithm is hopeful to be applied to the underground environment for in the future.

This article is based on a paper presented at an international conference in Norway. Having read in ICT about the work of the Small Firms Management Centre at the Sheffield Polytechnic, Gary Hansen wrote to us to tell us about similar work underway in the US.

The purpose of this paper is to examine the structure of juvenile psychopathy, as measured by the Psychopathy Checklist: Youth Version (PCL: YV).

Using a sample of 2,042 male youths from the USA, Canada, and the UK, the study was a conceptual replication of Bishopp and Hare’s (2008) multidimensional scaling (MDS) analysis of adult male offenders assessed with the Psychopathy Checklist-Revised.

The scaling analyses generally replicated those obtained by Bishopp and Hare, providing support for a multidimensional, four-factor model of juvenile psychopathy similar to that obtained with adults. However, a small number of items fell outside their predicted regions. Slight differences in the structure of juvenile psychopathy were found for incarcerated and supervised samples of youth, with the four-factor model breaking down slightly for the supervised sample. Item misplacements may indicate that certain items on the PCL: YV are being misinterpreted, reflect different dimensions for different samples, or cannot be reliably measured. Future research should examine these possibilities, with special attention being paid to supervised samples.

To the authors’ knowledge, this is one of the first known attempts to use MDS analysis to examine the psychopathy structures that emerge for male juvenile offenders. The greater nuances afforded by using MDS offer a more comprehensive understanding of psychopathy between incarcerated and supervised youth using the PCL: YV.

Multidimensional scaling (MDS) has often been utilised in retail store positioning and repositioning research. However, MDS results constrain store movement to existing perceptual dimensions. This work indicates that an assessment of higher level preference functions should be conducted before utilising MDS results in store positioning. Despite this limitation, the ability of MDS to reveal current perceptual criteria means that it can serve as a very useful diagnostic. In this regard, MDS can be utilised to determine if a new dimension has emerged from a given positioning, or whether consumers have changed the salience of their perceptual criteria, as a result of a positioning. It can also reveal if a referent store has moved in the appropriate direction (e.g. away from competitors and towards a gap that evidences utility).

The purpose of this paper is to propose a dedication-constraint-temptation (DCT) model to study the factors influencing customers’ loyalty to mobile data service (MDS) providers. The DCT model explicitly explores the important yet overlooked role of alternative attractiveness (the temptation-based mechanism) as a mediator and the boundary condition of their interrelationships (e.g. relationship length). The model also integrates new and established antecedents of customer-based brand equity (C-BBE) (the dedication-based mechanism) and switching barriers (the constraint-based mechanism).

The proposed model is tested using partial least squares–structural equation modeling with a sample of 331 MDS users.

The results indicate that C-BBE has an indirect effect on customer loyalty (via alternative attractiveness) in both relationship groups (shorter- vs longer-term). However, the indirect effect of switching barriers on customer loyalty only exists in longer established relationships. The results from multi-group analysis reveal that the effect of switching barriers on alternative attractiveness significantly differs across groups. In addition, customer value anticipation and procedural switching costs appear to be the most salient antecedents of C-BBE and switching barriers for both groups.

This study makes an incremental contribution by incorporating the temptation-based mechanism as a mediator and relationship length as a moderator into the dedication-constraint model. This study also extends the information systems and brand management literatures by demonstrating the strategic importance of customer value anticipation in the information and communication technology brand equity-building.

This project compared the use of an evidence‐based, systematically developed, standarised assessment instrument for community care with existing assessment instruments, in two social services departments. We conclude that assessment methods without proven reliability may jeopardise the interests of older people and the need to raise standards.

First feelings and first impressions largely govern and delimit the kinds of experiences one expects and seeks. This paper reviews the application of multidimensional scaling (MDS) to retail market research and discusses how MDS is able to map these impressions and hence explain the retail centre image held by consumers. It proposes that retail centre image is not just relevant to marketers and retailers, but is also of importance to property managers who need an informed and holistic consumer orientation in order to create and sustain property value. By means of illustration MDS is applied to data based on shopper evaluations of seven retail centre items and an interpretation the results is offered.

Localization is a fundamental problem in wireless sensor networks. In many applications, sensor location information is critical for data processing and meaning. While the global positioning system (GPS) can be used to determine mote locations with meter precision, the high hardware cost and energy requirements of GPS receivers often prohibit the ubiquitous use of GPS for location estimates. This high cost (in terms of hardware price and energy consumption) of GPS has motivated researchers to develop localization protocols that determine mote locations based on cheap hardware and localization algorithms. The purpose of this paper is to present a comprehensive review of wireless sensor network localization techniques, and provide a detailed overview for several distance‐based localization algorithms.

To provide a detailed summary of wireless sensor network localization algorithms, the authors outline a tiered classification system in which they first classify algorithms as distributed, distributed‐centralized, or centralized. From this broad classification, the paper then further categorizes localization algorithms using their protocol techniques. By utilizing this classification system, the authors are able to provide a survey of several wireless sensor network localization algorithms and summarize relative algorithm performance based on the algorithms' classification.

There are numerous localization algorithms available and the performance of these algorithms is dependent on network configuration, environmental variables, and the ranging method implemented. When selecting a localization algorithm, it is important to understand basic algorithm operation and expected performance. This tier‐based algorithm classification system can be used to gain a high‐level understanding of algorithm performance and energy consumption based on known algorithm characteristics.

Localization is a widely researched field and given the quantity of localization algorithms that currently exist, it is impossible to present a complete review of every published algorithm. Instead, the paper presents a holistic view of the current state of localization research and a detailed review of ten representative distance‐based algorithms that have diverse characteristics and methods. This review presents a new classification structure that may help researchers understand, at a high‐level, the expected performance and energy consumption of algorithms not explicitly addressed by our work.