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This paper presents an application of the fuzzy gradual rules in an epidemic study of canine rabies in São Paulo city, Brazil. A linguistic epidemiological model was elaborated through fuzzy rules built by the Extension Principle. We used both the inference method of Mamdani and of Dubois et al. The results were compared with real data from São Paulo and with another MISO Model, which is entirely based on expert knowledge presented in a previous work. Questions about application of fuzzy techniques in epidemiology, different inference methods and the Dubois et al. methodology are discussed.

Decisions about strategies to be implemented and organization design are important in having competitive advantage for companies. Although this statement widely takes place in management literature, very little research has been done on this subject in Turkey. The aim of this study is to examine the mediating role of competitive strategies in the impact of organizational flexibility on competitive advantage in order to extend about empirical literature of the structure-strategy-competitive advantage paradigm based on the resource-based view (RBV). In this context, the data were collected from the top 500 industrial enterprises in Turkey. The data were tested via structural equation model. The findings of our research show that the direct effect of organizational flexibility on competitive superiority was marginally significant and when three different competitive strategies were included between these two variables in the model, the marginally significant effect of organizational flexibility on the competitive advantage disappeared. In other words, the full mediating role of applied competition strategies in the effect of organizational flexibility on competitive advantage has been identified. Our findings are similar to the findings of other studies that consider organizational structure as a valuable resource and examine the relationship between the structure and the strategy according to the RBV.

The purpose of the study is to discover clusters or homogenous groups of work-from-home (WFH) Internet subscribers in the Philippines based on the attributes of speed, network quality, customer service, after-sales support, price, contract and value for money. Another objective is to determine if the formed clusters are related to the demographic profile.

A total of 275 internet subscribers from Metro Manila were surveyed. K-means cluster analysis using the Hartigan-Wong algorithm was performed on the data to generate the clusters.

Results generated four significant clusters, which were named service value expecters, average expecters, low expecters and high expecters. Most of the subscribers are under the high expecters, followed by the service value expecters. The age and income of the subscribers are the profile that can affect the formulation of clusters in the Internet service industry. Those people in the younger age groups can be seen as more demanding, while older people tend to be content with the Internet service. Counter-intuitively, people with higher income seem to be more easily satisfied with Internet service features, while those people with lower income seem to be more demanding. Educational attainment and the number of household members do not have a direct effect on the formulation of clusters of Internet subscribers.

This is the first study to use cluster analysis in identifying possible segments of WFH Internet subscribers based on Internet service attributes. Its value is the provision of market segments based on which can be the basis for formulating marketing strategies and policies for the telecommunications industry.

Proposes an application of fuzzy set theory to model epidemiological problems. Fuzzy logic has been revealed as a powerful predictive tool in the epidemiology of infectious diseases and some ideas are presented on how this could be done. This work presents an attempt to model the dynamics of rabies among a population of partially vaccinated dogs. This study demonstrates how a dynamical system can be modelled by fuzzy linguistic rules compared to the classical differential equations approach. The results are very encouraging and the approach through a more complex dynamical system is discussed in the final section.

The ‘hostile environment’ agenda of Government has effectively endorsed the deliberate exploitation of irregular migrants in the agricultural industry. This minor project of jurisprudence will provide two arguments in support of this hypothesis. Firstly, the vulnerabilities faced by irregular migrants are reinforced through the xenophobic narrative embodied within the law and the courts. The lack of priority afforded to modern slavery on the UK Policy Agenda has allowed a ‘grey labour market’ (1) to develop. Irregular migrants face a ‘precarity paradox’ and (2) they must avoid the carceral regime of immigration control by entering into unprotected and deliberately exploitative work. Secondly, a lacklustre attempt to remedy the corruption in the horticultural industry proves that the state is preoccupied, capitalising on irregular labour practice in the interests of state capital. Thus, the Government allows the commodification of workers within the supply chain to profit British businesses.

The Geography of Tourism in Spain is now at a par in terms of its scientific production with other European countries. Since the middle of the '80s the quality and volume of contributions is analogous to the rest of the European Union, although as a part of University Geography in Spain it has not achieved the level of dedication reached by other subjects considering the importance of tourist activities to the economy, the society and the territory of Spain. It could be said that the Geography of Tourism in Spain is in the international vanguard in dealing with Mediterranean coastal tourism, with the relationships between the residential real estate and tourism sectors and with aspects related to tourism and leisure in rural and protected areas.

While the Affordable Care Act (ACA) promised to reduce inequalities in insurance coverage between Latinos and non-Latinos by expanding coverage, it also excluded a large portion of noncitizen immigrants. Past research has demonstrated that among Latinos, further inequalities have developed between citizens and noncitizens after the ACA took effect, but it is unclear if this pattern is unique to Latinos or is evident among non-Latinos as well. I use data from the 2011 to 2016 waves of the National Health Interview Survey (NHIS) (n = 369,386) to test how the relationship between citizenship status (native citizen, naturalized citizen, or noncitizen) and insurance coverage changed after the ACA, adjusting for health, demographic, and socioeconomic factors. I disaggregate the analysis by ethnicity to test whether this change differs between Latinos and non-Latinos. The analysis finds that after the ACA, naturalized citizens across ethnic groups moved toward parity with native citizens in health insurance coverage while the benefits of the ACA for noncitizens were conditional on ethnicity. For non-Latinos, lacking citizenship became less disadvantageous for predicting insurance coverage while for Latinos, lacking citizenship became even more disadvantageous in predicting insurance coverage. This bifurcation among noncitizens by ethnicity implies that while the ACA has strengthened institutional boundaries between citizens and noncitizens, this distinction is primarily affecting Latinos. The conclusion offers considerations on how legal systems of stratification influence population health processes.

Purpose: Fear of deportation and its relationship to healthcare access has been less studied among immigrant Latinx men who have sex with men (MSM), a population at risk for HIV and characterized by their multiple minority statuses. The first step is to accurately measure their fear of deportation.

Approach: We used an exploratory sequential mixed methods design. Eligibility criteria were that research participants be ages 18–34 years; Latinx; cisgender male; having had sex with another male; residing in the District of Columbia metro area; and not a US citizen or legal permanent resident. In Study 1, we used in-depth interviews and thematic analysis. Using participants' interview responses, we inductively generated 15 items for a fear of deportation scale. In Study 2, we used survey data to assess the scale's psychometric properties. We conducted independent samples t-test on the associations between scale scores and barriers to healthcare access.

Findings: For the 20 participants in Study 1, fear of deportation resulted in chronic anxiety. Participants managed their fear through vigilance, and behaviors restricting their movement and social network engagement. In Study 2, we used data from 86 mostly undocumented participants. The scale was internally consistent (α = 0.89) and had a single factor. Those with higher fear of deportation scores were significantly more likely to report avoiding healthcare because they were worried about their immigration status (p = 0.007).

Originality: We described how fear of deportation limits healthcare access for immigrant Latinx MSM.

Research implications: Future research should examine fear of deportation and HIV risk among immigrant Latinx MSM.

The construction sector has significantly evolved in recent decades, in parallel with a huge increase in the amount of data generated and exchanged in any construction project. These data need to be managed in order to complete a successful project in terms of quality, cost and schedule in the the context of a safe project environment while appropriately organising many construction documents.

However, the origin of these data is very diverse, mainly due to the sector’s characteristics. Moreover, these data are affected by uncertainty, complexity and diversity due to the imprecise nature of the many factors involved in construction projects. As a result, construction project data are associated with large, irregular and scattered datasets.

The objective of this chapter is to introduce an approach based on a fuzzy multi-dimensional model and on line analytical processing (OLAP) operations in order to manage construction data and support the decision-making process based on previous experiences. On one hand, the proposal allows for the integration of data in a common repository which is accessible to users along the whole project’s life cycle. On the other hand, it allows for the establishment of more flexible structures for representing the data of the main tasks in the construction project management domain. The incorporation of this fuzzy framework allows for the management of imprecision in construction data and provides easy and intuitive access to users so that they can make more reliable decisions.

To investigate the association between subjective dimensions of socioeconomic status and psychological distress, paying particular attention to Latino subgroup differences.

We used data from the Latino sample (N = 2,554) of the National Latino and Asian American Study (NLAAS). For our main analyses, we conducted a series of weighted multivariable linear regressions.

The results showed that subjective social status was associated with reduced psychological distress.

There are several key study limitations that warrant consideration. Chiefly, data utilized were cross-sectional; thus, it is unclear whether subjective socioeconomic status (SES) precedes or follows psychological distress.

Overall, our study makes several contributions to the sociological study of mental health differentials among Latinos. We show the importance of the association between subjective SES indicators and psychological distress. We also demonstrate how the associations analyzed in this study varied by Latino subethnicity, which we argue is an important step to fully understand the different social processes associated with the mental health of different Latina/o groups.