Hybrid cooperation for machine‐to‐machine data collection in hierarchical smart building networks

Wireless sensor networks (WSNs) utilize large numbers of wireless sensor nodes to perform close-range sensing and thus enhance sensing qualities. In typical sensing applications, data packets are flowing from sensor nodes to a base station in a many-to-one network structure. To keep event detection delay at a low value, in applications that require occasion data snapshots, it is always desirable to reduce the duration of a data collection process (DCP). Conversely, for applications that require continuous monitoring, the number of DCPs completed in a given period of time is important for reconstructing an accurate data trend. In this paper, a delay-aware network structure is proposed for WSNs with consecutive DCPs. The proposed network structure is able to increase the number of DCPs per unit time without imposing extra delay on each single DCP. A multistage network formation algorithm is proposed to construct the proposed network structure while keeping communication distances among sensor nodes at low values. Simulation results shows that the proposed network structure can provide significant improvements on data collection rates without increasing data collection durations. © 2012 IEEE.

It is well established that network structure strongly influences infectious disease dynamics. However, little is known about how the network structure impacts the cost-effectiveness of disease control strategies. We evaluated partner management strategies to address bacterial sexually transmitted infections (STIs) as a case study to explore the influence of the network structure on the optimal disease management strategy. We simulated a hypothetical bacterial STI spread through 4 representative network structures: random, community-structured, scale-free, and empirical. We simulated disease outcomes (prevalence, incidence, total infected person-months) and cost-effectiveness of 4 partner management strategies in each network structure: routine STI screening alone (no partner management), partner notification, expedited partner therapy, and contact tracing. We determined the optimal partner management strategy following a cost-effectiveness framework and varied key compliance parameters of partner management in sensitivity analysis. For the same average number of contacts and disease parameters in our setting, community-structured networks had the lowest incidence, prevalence, and total infected person-months, whereas scale-free networks had the highest without partner management. The highly connected individuals were more likely to be reinfected in scale-free networks than in the other network structures. The cost-effective partner management strategy depended on the network structures, the compliance in partner management, the willingness-to-pay threshold, and the rate of external force of infection. Our findings suggest that contact network structure matters in determining the optimal disease control strategy in infectious diseases. Information on a population's contact network structure may be valuable for informing optimal investment of limited resources.

Social networks and social support can influence older adults' depressive symptoms, but depressive symptoms can also influence network maintenance. This study examined longitudinal relationships between social network structure, social support, and depressive symptoms. Data are from Waves 1 (2005-2006) and 2 (2010-2011) of the National Social Life, Health, and Aging Project, a longitudinal study on health and social factors of older adults. Models examining: (i) the influence of T1 network structure and T1 social support on T2 depressive symptoms; (ii) the influence of T1 depressive symptoms and T1 network structure on T2 social support; and (iii) the influence of T1 depressive symptoms and T1 social support on T2 network structure, were estimated using ordinary least squares lagged dependent variable regression models. Evidence of reciprocal associations between social support and depressive symptoms were found, as well as social support and the number of close ties and frequency of contact. No clear reciprocal associations between social network structure and depressive symptoms were found, although density was associated with later depressive symptoms, and depressive symptoms were associated with later number of close ties. The reciprocal relationship between network structure and depressive symptoms is weak, whereas social support is strongly related to both depression and network structure, suggesting the importance of having supportive ties in an older adult's personal network for positive mental health.

Purpose This paper analyzes employees’ perceptions of data collection processes for human resource analytics (HRA). More specifically, we study the effect that information sharing practices have on employees’ attributions (i.e. benevolent vs malevolent) through the perceived legitimacy of data collection and monitoring processes. Moreover, we investigate whether employees’ emotional reaction (i.e. fear of datafication) depends on their perceived legitimacy and attributions. Design/methodology/approach The research is based on a sample of 259 employees operating for an Italian consulting firm that developed and implemented HRA processes in the last 3 years. The hypothesized model has been tested using structural equation modeling (SEM) on Stata 14. Findings This paper demonstrates the mediating role of perceived legitimacy in the relationship between information sharing practices and employees’ benevolent and malevolent attributions about data collection and monitoring processes for HRA practices. Results also reveal that perceived legitimacy predicts employees’ fear of datafication, with benevolent attributions that partially mediate this relationship. Practical implications This research indicates that employees perceive, try to make sense of and emotionally react to HRA processes. Moreover, we reveal the crucial role of information sharing practices and perceived legitimacy in determining employees’ attributions and emotional reactions to data collection and monitoring processes. Originality/value Combining human resource (HR) attributions, HR system strength, information processing and signaling theories, this work explores employees’ perception, attributive processes and emotional reactions to data collection processes for HRA practices.

Information about mental health systems is essential for mental health planning to reduce the burden of neuropsychiatric disorders. Unfortunately, many low- and middle-income countries lack systematic information on their mental health systems. The objectives, scope, structure, and contents of mental health assessment and monitoring instruments commonly used in high-income countries may not be appropriate for use in middle- and low-income countries. The World Health Organization (WHO) has recently developed the WHO Assessment Instrument for Mental Health Systems (WHO-AIMS), a comprehensive assessment tool for mental health systems designed for middle- and low-income countries. WHO-AIMS was developed through an iterative process that included input from in-country and international experts on the clarity, content, validity, and feasibility of the instrument, as well as a pilot trial. The resulting instrument, WHO-AIMS 2.2, consists of six domains: policy and legislative framework, mental health services, mental health in primary care, human resources, public information and links with other sectors, and monitoring and research. These domains address the ten recommendations of the World Health Report 2001 through 28 facets and 155 items. All six domains need to be assessed to form a basic, yet broad, picture of a mental health system, with a focus on health sector activities. WHO-AIMS provides essential information for mental health policy and service delivery. Countries will be able to develop information-based mental health policy and plans with clear baseline information and targets. Moreover, they will be able to monitor progress in implementing reform policies, providing community services, and involving consumers, families, and other stakeholders in mental health promotion, prevention, care and rehabilitation. This article provides an overview of the rationale, development process, and potential uses and benefits of WHO-AIMS.

The deformation and toughness of amorphous glassy polymers is discussed in terms of both the molecular network structure and the microscopic structure. Two model systems were taken into consideration: polystyrene-poly(2,6-dimethyl-1,4-phenylene ether) blends (PS-PPE) and epoxides based on diglycidyl ether of bisphenol A (DGEBA). The network structure of the thermoplastic PS-PPE system could be varied systematically by changing the relative volume fractions of PS (low entanglement density, v e=3 × 1025 chains m−3) and PPE (v e=13 × 10s25 chains m−3) in this miscible blend. The crosslink density, v c, of the DGEBA system could be set by selecting various epoxide monomer molecular weights (8 × 1025 ⩽ v c ⩽ 235 × 1025 chains m−3). The microscopic structure at length scales of 50–300 nm was controlled by the addition of different amounts of non-adhering core-shell-rubber particles having a constant diameter. Thoughness is mainly determined by the maximum macroscopic draw ratio since the yield stress of most polymers approximately is identical (50–80 MPa). It is shown, based on the analysis of experimental data published in literature, that the theoretical maximum draw ratio, derived from the maximum (entanglement or crosslink) network deformation, is obtained macroscopically when the characteristic length scale of the microstructure of the material is below a certain value, i.e., the critical matrix ligament thickness between added nonadhering rubbery particles (\s`holes\s`). The value of the critical matrix ligament thickness (IDc) uniquely depends on the network structure: at an increasing network density, IDc increases independent of the nature of the network structure (entanglements or crosslinks). A simple model is presented, based on an energy criterion, to account for the phenomenon of a critical ligament thickness.

There are various phenomena of malicious information spreading in the real society, which cause many negative impacts on the society. In order to better control the spreading, it is crucial to reveal the influence of network structure on network spreading. Motifs, as fundamental structures within a network, play a significant role in spreading. Therefore, it is of interest to investigate the influence of the structural characteristics of basic network motifs on spreading dynamics. Considering the edges of the basic network motifs in an undirected network correspond to different tie ranges, two edge removal strategies are proposed, short ties priority removal strategy and long ties priority removal strategy. The tie range represents the second shortest path length between two connected nodes. The study focuses on analyzing how the proposed strategies impact network spreading and network structure, as well as examining the influence of network structure on network spreading. Our findings indicate that the long ties priority removal strategy is most effective in controlling network spreading, especially in terms of spread range and spread velocity. In terms of network structure, the clustering coefficient and the diameter of network also have an effect on the network spreading, and the triangular structure as an important motif structure effectively inhibits the spreading.

4th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2012, Sanya, 10-12 October 2012

Epileptic seizures are generally classified as either focal or generalized. It had been traditionally assumed that focal seizures imply localized brain abnormalities, whereas generalized seizures involve widespread brain pathologies. However, recent evidence suggests that large-scale brain networks are involved in the generation of focal seizures, and generalized seizures can originate in localized brain regions. Herein we study how network structure and tissue heterogeneities underpin the emergence of focal and widespread seizure dynamics. Mathematical modeling of seizure emergence in brain networks enables the clarification of the characteristics responsible for focal and generalized seizures. We consider neural mass network dynamics of seizure generation in exemplar synthetic networks and we measure the variance in ictogenicity across the network. Ictogenicity is defined as the involvement of network nodes in seizure activity, and its variance is used to quantify whether seizure patterns are focal or widespread across the network. We address both the influence of network structure and different excitability distributions across the network on the ictogenic variance. We find that this variance depends on both network structure and excitability distribution. High variance, i.e., localized seizure activity, is observed in networks highly heterogeneous with regard to the distribution of connections or excitabilities. However, networks that are both heterogeneous in their structure and excitability can underlie the emergence of generalized seizures, depending on the interplay between structure and excitability. Thus, our results imply that the emergence of focal and generalized seizures is underpinned by an interplay between network structure and excitability distribution.

One component of the School-to-Work Opportunities Act was the collection of data on program activities and participants. Ms. White and Mr. Medrich look back on the process that was used, explore lessons learned, and offer suggestions for improving such efforts in the future. AS A TIME-LIMITED initiative with a statutorily defined sunset, the School-to-Work Opportunities Act of 1994 (STWOA) provides a unique window through which to view the life span of a performance measurement system -- design through gestation and early implementation to wide-scale operation and, ultimately, closure. In this article we explore some of the important lessons learned the design and implementation of the STWOA performance measurement process and offer recommendations to the policy makers who will help shape future data- collection mandates. Lessons Learned 1. Time and money are needed to build effective performance measurement systems; neither was available in sufficient quantities under the STWOA. While the STWOA explicitly called for the creation of a performance measurement system, it did not target additional resources to support state and local data-collection activities. States and local partnerships were forced to cobble together their processes of data collection with limited resources and, often, with limited expertise. The time line under which the mandated performance measurement system was developed and implemented was ambitious, if not unrealistic. A task force of state school-to-work (STW) directors gave their collective advice on the development of a data-collection instrument, and the resulting Progress Measures Survey was field-tested less than 18 months after the enactment of the STWOA. Even so, more than two years elapsed before a substantial number of local partnerships were in a position to collect any data. The Progress Measures Survey asked local STW partnerships to report quantitative data on a number of items about which information had never before been collected. Moreover, partnerships had to collect data many sources and create mechanisms for documenting participation in a range of school- and work-based learning activities. For many partnerships, the first collection of progress measures data was a learning experience. They discovered which institutions and individuals had what types of data and determined which types of data had to be collected from scratch. 2. Federal, state, and local audiences have different data needs and interests. Federal, state, and local officials think differently about the purposes and processes of data collection. Their differing expectations and perspectives add yet another level of complexity to efforts to build effective systems to measure performance. To produce meaningful data, measurement needs to occur at the level at which goals are set and activities are defined. This was not necessarily the case with STWOA data-collection efforts. Although local partnerships, and ultimately individual schools, were asked to collect data, the Progress Measures Survey reflected federal and state priorities. Local practitioners had little or no input into the indicators used or the questions asked. Table 1 summarizes key differences in perspective that emerged a qualitative study of STW data-collection experiences. The first column identifies specific topics or issues related to the collection and utility of the data requested in the Progress Measures Survey. The remaining columns present federal, state, and local perspectives on each topic. This table illustrates the mismatch of perspectives and the lack of philosophical alignment in five crucial areas. The different needs and wants at each level of governance were based largely on the context and circumstances in which officials and practitioners operate. In the collection of progress measures data, these differences played out as unresolved -- and, perhaps, irreconcilable -- tensions. …

Ayushman Bharat Pradhan Mantri Jan Aarogya Yojana (AB PM-JAY) has enabled the Government of India to become a strategic purchaser of health care services from private providers. To generate base cost evidence for evidence-based policymaking the Costing of Health Services in India (CHSI) study was commissioned in 2018 for the price setting of health benefit packages. This paper reports the findings of a process evaluation of the cost data collection in the private hospitals. The process evaluation of health system costing in private hospitals was an exploratory survey with mixed methods (quantitative and qualitative). We used three approaches-an online survey using a semi-structured questionnaire, in-depth interviews, and a review of monitoring data. The process of data collection was assessed in terms of time taken for different aspects, resources used, level and nature of difficulty encountered, challenges and solutions. The mean time taken for data collection in a private hospital was 9.31 (± 1.0) person months including time for obtaining permissions, actual data collection and entry, and addressing queries for data completeness and quality. The longest time was taken to collect data on human resources (30%), while it took the least time for collecting information on building and space (5%). On a scale of 1 (lowest) to 10 (highest) difficulty levels, the data on human resources was the most difficult to collect. This included data on salaries (8), time allocation (5.5) and leaves (5). Cost data from private hospitals is crucial for mixed health systems. Developing formal mechanisms of cost accounting data and data sharing as pre-requisites for empanelment under a national insurance scheme can significantly ease the process of cost data collection.

IntroductionAyushman Bharat Pradhan Mantri Jan Aarogya Yojana (AB PM-JAY) has enabled the Government of India to become a strategic purchaser of health care services from private providers. To generate base cost evidence for evidence-based policymaking the Costing of Health Services in India (CHSI) study was commissioned in 2018 for the price setting of health benefit packages. This paper reports the findings of a process evaluation of the cost data collection in the private hospitals.MethodsThe process evaluation of health system costing in private hospitals was an exploratory survey with mixed methods (quantitative and qualitative). We used three approaches–an online survey using a semi-structured questionnaire, in-depth interviews, and a review of monitoring data. The process of data collection was assessed in terms of time taken for different aspects, resources used, level and nature of difficulty encountered, challenges and solutions.ResultsThe mean time taken for data collection in a private hospital was 9.31 (± 1.0) person months including time for obtaining permissions, actual data collection and entry, and addressing queries for data completeness and quality. The longest time was taken to collect data on human resources (30%), while it took the least time for collecting information on building and space (5%). On a scale of 1 (lowest) to 10 (highest) difficulty levels, the data on human resources was the most difficult to collect. This included data on salaries (8), time allocation (5.5) and leaves (5).DiscussionCost data from private hospitals is crucial for mixed health systems. Developing formal mechanisms of cost accounting data and data sharing as pre-requisites for empanelment under a national insurance scheme can significantly ease the process of cost data collection.

An efficient complex network embedding model for hierarchical networks

Wireless sensor networks utilize large numbers of wireless sensor nodes to collect information from their sensing terrain. Wireless sensor nodes are battery-powered devices. Energy saving is always crucial to the lifetime of a wireless sensor network. Recently, many algorithms are proposed to tackle the energy saving problem in wireless sensor networks. In these algorithms, however, data collection efficiency is usually compromised in return for gaining longer network lifetime. There are strong needs to develop wireless sensor networks algorithms with optimization priorities biased to aspects besides energy saving. In this paper, a delay-aware data collection network structure for wireless sensor networks is proposed. The objective of the proposed network structure is to minimize delays in the data collection processes of wireless sensor networks. Two network formation algorithms are designed to construct the proposed network structure in a centralized and a decentralized approach. Performances of the proposed network structure are evaluated using computer simulations. Simulation results show that, when comparing with other common network structures in wireless sensor networks, the proposed network structure is able to shorten the delays in the data collection process significantly.

Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds