Search results

The early care and education (ECE) workforce plays a pivotal role in shaping early childhood developmental trajectories and simultaneously experiences significant mental health disparities. The purpose of this study is to investigate how social determinants of health and external stressors are associated with the mental health of ECE staff, which represent a low-resourced segment of the workforce; how psychological capital (psycap) can mitigate these associations.

The authors administered an 89-item survey to 332 ECE staff employed in 42 Head Start centers in the USA. The authors ran three hierarchical linear regression models to analyze associations between social determinants of health, external sources of stress, psycap and potential moderation effects and mental health outcomes.

Individuals experiencing greater finance-related stress reported 0.15 higher scores on the depression scale and 0.20 higher scores on the anxiety scale than those experiencing less finance-related stress (p < 0.05). Individuals experiencing greater work-related stress reported 1.26 more days of poorer mental health in the past month than those experiencing less work-related stress (p < 0.01). After controlling for all sociodemographic variables and sources of stress, psycap was significantly and negatively associated with depressive symptomology (b-weight = −0.02, p < 0.01) and the number of poor mental health days reported in the past month (b-weight = −0.13, p < 0.05). Moderation models suggest that higher levels of psycap may mitigate the association between work-related stress and the number of poor mental health days reported in the past month (b-weight = −0.06, p = 0.02).

The implications of these findings suggest a need for policy change to mitigate social determinants of health and promote pay equity and multi-level interventio ns that target workplace-related stressors and psycap to combat poor mental health of the ECE workforce.

Presence of innumerable sensors, complex deduction of contexts from sensor data, and reusability of contextual information impose the requirement of middleware for context aware computing. Smart applications, hosted in myriad devices (e.g. PDA, mobile, PCs), acquire different contexts from the middleware and act intelligently based on the available contexts in a context‐aware computing environment. As the system grows larger, scalable delivery of contexts from the middleware to numerous context‐aware applications will be inevitable. However, pure unicast based or pure broadcast‐based dissemination cannot provide high scalability as well as low‐average latency. The purpose of this paper is to present a scalable context delivery mechanism for the middlewares to facilitate the development of larger context‐aware computing systems.

The proposed scheme is based on hybrid data dissemination technique where the most frequently requested data (e.g. HOT contexts) are delivered through multicast and the rest (e.g. COLD contexts) are delivered through unicast to reduce network traffic. The paper dynamically prioritizes and classifies the HOT and COLD context data depending on the number of requests and longest waiting time. Moreover, the division of bandwidth between the delivery of HOT and COLD contexts reduces average latency. Polling traffic is decreased by incorporating leasing mechanism. Extensive simulation is conducted to evaluate the proposed scheme.

The mechanism dynamically prioritizes and classifies the hot and cold context data depending on the request rate and longest waiting time. The solution addresses the push popularity problem that occurs in the passive as the passive clients access data without sending explicit requests. The leasing mechanism is incorporated to reduce the periodical requests (polling) for better performance.

The paper is of value in presenting a scalable context delivery mechanism for the middlewares to facilitate the development of larger context‐aware computing systems and also in presenting implementation details of a prototype that is developed using Jini framework and Java reliable multicast service (JRMS) library.

The purpose of this paper is to identify factors, within a framework for integration, associated with healthcare utilization (primary care use, dental care, and insurance coverage) for Colorado refugees, by gender.

The Refugee Integration Survey and Evaluation project was a four-year longitudinal study of refugees that resettled in Colorado beginning in 2011. Refugees from Burma and Bhutan were used in this secondary data analysis. Various integration domains were explored as predictors, across gender groups, of the healthcare utilization outcome variables (physical exam in the past 12 months, dental exam in the past 12 months, and current healthcare coverage) using bivariate and multivariate logistic regression analyses.

In 2015, 73.1 percent of the sample reported accessing primary health care in the past year, and only 13.2 percent used dental care services. Nearly three-quarters reported having health insurance at the time of survey. In the adjusted models, there was a strong positive association between the outcome variable “physical exam” and the predictor variables “employment and economic self-sufficiency” (OR=0.70, p<0.001), “social bonding” (OR=3.73, p<0.001), and “safety and stability” (OR=2.23, p<0.001). Additionally, education and training predicted dental visit (OR=2.06, p<0.01). None of the integration domains were statistically significant predictors of dental visits in the adjusted models.

This study offers insights about facilitators and barriers to healthcare utilization uptake after resettling in a major US city. These findings can be used by agencies and governmental organizations to best tailor healthcare services and promotion of those services for this vulnerable population.

The purpose of this paper is to characterise a number of current and future computing environments and summarises their resource discovery requirements. It then seeks to analyse, with respect to the requirements of each environment, several established service discovery protocols and some newer protocols that are still in the research domain. In addition, the key features of a new resource discovery protocol that has been developed to operate with heterogeneous computing environments are described.

A comprehensive literature survey was undertaken, highlighting the shortcomings of existing resource discovery protocols with respect to large pervasive computing environments. Given the identified gaps in existing protocols, an alternative protocol is suggested.

The main findings of this paper relate to the identified shortcomings of existing resource discovery protocols. It was also found that a hybrid resource discovery protocol capable of spanning dynamic, mobile computing environments and more stable ones was able to overcome many of the challenges presented by large‐scale pervasive computing environments.

This paper presents comprehensive literature survey of the state‐of‐the‐art in resource discovery protocols, pointing out some of the problems that are not solved. The paper describes the design of an alternative protocol, and presents an evaluation of it. The pervasive computing research community can draw upon the survey and evaluation to guide the design of future resource discovery protocols for the increasingly dynamic world in which we live.

We obtained information on China Gerui from secondary published sources, including annual reports downloaded from the Securities and Exchange Commission’s (SEC) EDGAR database, news sites and newspapers, the company’s website and journal articles. One of the authors visited the China Gerui plant in Henan, China.

China Gerui, a Chinese metal fabrication company, enjoyed exponential growth because of its location, product innovation and ability to move up the value chain. At the height of its success, the company listed on the Nasdaq and had plans to raise capital to fund ambitious expansion plans. Unfortunately, four years after listing on Nasdaq, the company received a letter from the listing qualifications department notifying China Gerui that they were not in compliance with Nasdaq’s filing requirements because it had not filed its Form 20-F. Now, the company had only five days to decide whether to request an appeal of the letter.

This case is best suited for higher-level undergraduate accounting and finance courses such as intermediate accounting, auditing, international accounting, financial statement analysis, corporate finance and investments analysis. It is especially appropriate for graduate-level global accounting and advanced financial statement analysis courses. In these courses, the best placement is after coverage of SEC regulations and requirements for financial statement reporting and disclosure. Moreover, the case may be used as a tool to demonstrate the step-by-step process for searching and retrieving information from a public company’s filings through the SEC’s EDGAR database.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

The practice of facility management (FM) has been evolving with the rapid development of pervasive sensing technologies (PSTs) such as sensors, automatic identification (auto-ID), laser scanning and photogrammetry. Despite the proliferation of research on the use of PSTs for FM, a comprehensive review of such research is missing from the literature. This study aims to cover the knowledge void by examining the status quo and challenges of the selected PSTs with a focus on FM.

This paper reviewed 204 journal papers recounting cases of using PSTs for FM. The reviewed papers were extracted from Elsevier Scopus database using the advanced search.

Findings of this study revealed that PSTs and FM applications form a many-to-many mapping, i.e. one PST could facilitate many FM applications, and one application can also be supported by various PSTs. It is also found that energy modeling and management is the most referred purpose in FM to adopt PSTs, while space management, albeit important, received the least attention. Five challenges are identified, which include high investment on PSTs, data storage problem, absence of proper data exchange protocols for data interoperability, a lack of mature data processing methods for data utilization and privacy of users.

This paper paints a full picture of PSTs adoption for FM. It pinpoints the promising explorations for tackling the key challenges to future development.

Imagine a world without turnarounds – just well‐managed companies. Strategic exploration is the means to achieving strategic resilience. But how can senior managers build organizations where strategic exploration is the rule rather than the exception? First, by exhibiting the courage to explore, and second, by confronting the uncomfortable truths that such exploration might uncover. The article presents a simple but powerful framework, conceived by the Woodside Institute, for strategic exploration. The framework identifies which discoveries merit most attention (“strategic breakthroughs”), which deserve further consideration (“self‐discoveries” and “windows of opportunity”), and which will lead to the back of beyond (“duds”). Examples are drawn from IBM, AT&T, Intel, Sony, Cisco, Hewlett‐Packard, Nokia, Sun Microsystems, and the Coca‐Cola Company.

The purpose of the study is distributed generation planning in a radial delivery framework to identify an appropriate location with a suitable rating of DG units energized by renewable energy resources to scale back the power loss and to recover the voltage levels. Though several algorithms have already been proposed through the target of power loss reduction and voltage stability enhancement, further optimization of the objectives is improved by using a combination of heuristic algorithms like DE and particle swarm optimization (PSO).

The identification of the candidate buses for the location of DG units and optimal rating of DG units is found by a combined differential evolution (DE) and PSO algorithm. In the combined strategy of DE and PSO, the key merits of both algorithms are combined. The DE algorithm prevents the individuals from getting trapped into the local optimum, thereby providing efficient global optimization. At the same time, PSO provides a fast convergence rate by providing the best particle among the entire iteration to obtain the best fitness value.

The proposed DE-PSO takes advantage of the global optimization of DE and the convergence rate of PSO. The different case studies of multiple DG types are carried out for the suggested procedure for the 33- and 69-bus radial delivery frameworks and a real 16-bus distribution substation in Tamil Nadu to show the effectiveness of the proposed methodology and distribution system performance. From the obtained results, there is a substantial decrease in the power loss and an improvement of voltage levels across all the buses of the system, thereby maintaining the distribution system within the framework of system operation and safety constraints.

A comparison of an equivalent system with the DE, PSO algorithm when used separately and other algorithms available in literature shows that the proposed method results in an improved performance in terms of the convergence rate and objective function values. Finally, an economic benefit analysis is performed if a photo-voltaic based DG unit is allocated in the considered test systems.