Journal articles: 'Deployment models'

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B. Patel, Prof Hiral, and Prof Nirali Kansara. "Cloud Computing Deployment Models: A Comparative Study." International Journal of Innovative Research in Computer Science & Technology 9, no. 2 (March 2021): 45–50. http://dx.doi.org/10.21276/ijircst.2021.9.2.8.

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Howick, R. S., and M. Pidd. "Sales force deployment models." European Journal of Operational Research 48, no. 3 (October 1990): 295–310. http://dx.doi.org/10.1016/0377-2217(90)90413-6.

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Sriningsih, Riry, Muhammad Subhan, and Minora Longgom Nasution. "Analysis of torch deployment models." Journal of Physics: Conference Series 1317 (October 2019): 012013. http://dx.doi.org/10.1088/1742-6596/1317/1/012013.

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Rimbaud, Loup, Frédéric Fabre, Julien Papaïx, Benoît Moury, Christian Lannou, Luke G. Barrett, and Peter H. Thrall. "Models of Plant Resistance Deployment." Annual Review of Phytopathology 59, no. 1 (August 25, 2021): 125–52. http://dx.doi.org/10.1146/annurev-phyto-020620-122134.

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Owing to their evolutionary potential, plant pathogens are able to rapidly adapt to genetically controlled plant resistance, often resulting in resistance breakdown and major epidemics in agricultural crops. Various deployment strategies have been proposed to improve resistance management. Globally, these rely on careful selection of resistance sources and their combination at various spatiotemporal scales (e.g., via gene pyramiding, crop rotations and mixtures, landscape mosaics). However, testing and optimizing these strategies using controlled experiments at large spatiotemporal scales are logistically challenging. Mathematical models provide an alternative investigative tool, and many have been developed to explore resistance deployment strategies under various contexts. This review analyzes 69 modeling studies in light of specific model structures (e.g., demographic or demogenetic, spatial or not), underlying assumptions (e.g., whether preadapted pathogens are present before resistance deployment), and evaluation criteria (e.g., resistance durability, disease control, cost-effectiveness). It highlights major research findings and discusses challenges for future modeling efforts.

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BUSHEHRIAN, OMID. "SOFTWARE PERFORMANCE ENGINEERING BY SIMULATED-BASED OBJECT DEPLOYMENT." International Journal of Software Engineering and Knowledge Engineering 23, no. 02 (March 2013): 211–21. http://dx.doi.org/10.1142/s0218194013500058.

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The object deployment of a distributed software has a great impact on its performance. In this paper an analytical model for performance evaluation of different object deployments, is presented. The key advantage of the proposed model over the traditional Queuing Network models is the usefulness in the deployment optimization when the search space is huge and automatic instantiation of Queuing performance models corresponding to an object deployment is costly. Since our model produces an optimal deployment corresponding to each input load separately, the runtime behavior of the software corresponding to each input load should be profiled using simulation first. In this paper a translation scheme for generating the simulate-able Labeled Transition Systems (LTS) from scenarios is also presented. Moreover, two deployment algorithms (a GA-based and an INLP-based) are implemented and the results are compared.

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Perakis, Anastassions N., and Nikiforos Papadakis. "Fleet deployment optimization models. Part 1." Maritime Policy & Management 14, no. 2 (January 1987): 127–44. http://dx.doi.org/10.1080/03088838700000015.

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Vinayak, Kalluri, and Rambabu Kodali. "Benchmarking the quality function deployment models." Benchmarking: An International Journal 20, no. 6 (October 21, 2013): 825–54. http://dx.doi.org/10.1108/bij-07-2011-0052.

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Eid, Mustafa I. M., Ibrahim M. Al-Jabri, and M. Sadiq Sohail. "Selection of Cloud Delivery and Deployment Models." International Journal of Decision Support System Technology 10, no. 4 (October 2018): 17–32. http://dx.doi.org/10.4018/ijdsst.2018100102.

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Research interests on cloud computing adoption and its effectiveness in terms of cost and time has been increasing. However, one of the challenging decisions facing management in adopting cloud services is taking on the right combinations of cloud service delivery and deployment models. A comprehensive review of literature revealed a lack of research addressing this selection decision problem. To fill this research gap, this article proposes an expert system approach for managers to decide on the right combination of service delivery and deployment model selection. The article first proposes a rule-based expert system prototype, which provides advice based on a set of factors that represent the organizational conditions and requirements pertaining to cloud computing adoption. Next, the authors evaluate the system prototype. Lastly, the article concludes with a discussion of the results, its practical implications, limitations, and further research directions.

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Kim, Kwang-Jae, Herbert Moskowitz, Anoop Dhingra, and Gerald Evans. "Fuzzy multicriteria models for quality function deployment." European Journal of Operational Research 121, no. 3 (March 2000): 504–18. http://dx.doi.org/10.1016/s0377-2217(99)00048-x.

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BALASUBRAMANIAN, KRISHNAKUMAR, ANIRUDDHA GOKHALE, YUEHUA LIN, JING ZHANG, and JEFF GRAY. "WEAVING DEPLOYMENT ASPECTS INTO DOMAIN-SPECIFIC MODELS." International Journal of Software Engineering and Knowledge Engineering 16, no. 03 (June 2006): 403–24. http://dx.doi.org/10.1142/s021819400600280x.

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Domain-specific models increase the level of abstraction used to develop large-scale component-based systems. Model-driven development (MDD) approaches (e.g., Model-Integrated Computing and Model-Driven Architecture) emphasize the use of models at all stages of system development. Decomposing problems using MDD approaches may result in a separation of the artifacts in a way that impedes comprehension. For example, a single concern (such as deployment of a distributed system) may crosscut different orthogonal activities (such as component specification, interaction, packaging and planning). To keep track of all entities associated with a component, and to ensure that the constraints for the system as a whole are not violated, a purely model-driven approach imposes extra effort, thereby negating some of the benefits of MDD. This paper provides three contributions to the study of applying aspect-oriented techniques to address the crosscutting challenges of model-driven component-based distributed systems development. First, we identify the sources of crosscutting concerns that typically arise in model-driven development of component-based systems. Second, we describe how aspect-oriented model weaving helps modularize these crosscutting concerns using model transformations. Third, we describe how we have applied model weaving using a tool called the Constraint-Specification Aspect Weaver (C-SAW) in the context of the Platform-Independent Component Modeling Language (PICML), which is a domain-specific modeling language for developing component-based systems. A case study of a joint-emergency response system is presented to express the challenges in modeling a typical distributed system. Our experience shows that model weaving is an effective and scalable technique for dealing with crosscutting aspects of component-based systems development.

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Hungness, Derek, and Raj Bridgelall. "Model Contrast of Autonomous Vehicle Impacts on Traffic." Journal of Advanced Transportation 2020 (August 14, 2020): 1–10. http://dx.doi.org/10.1155/2020/8935692.

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The adoption of connected and autonomous vehicles (CAVs) is in its infancy. Therefore, very little is known about their potential impacts on traffic. Meanwhile, researchers and market analysts predict a wide range of possibilities about their potential benefits and the timing of their deployments. Planners traditionally use various types of travel demand models to forecast future traffic conditions. However, such models do not yet integrate any expected impacts from CAV deployments. Consequently, many long-range transportation plans do not yet account for their eventual deployment. To address some of these uncertainties, this work modified an existing model for Madison, Wisconsin. To compare outcomes, the authors used identical parameter changes and simulation scenarios for a model of Gainesville, Florida. Both models show that with increasing levels of CAV deployment, both the vehicle miles traveled and the average congestion speed will increase. However, there are some important exceptions due to differences in the road network layout, geospatial features, sociodemographic factors, land-use, and access to transit.

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So, C. J., C. A. Alfano, L. A. Riviere, and P. J. Quartana. "0273 Residual Sleep Difficulties During Reset Operations Predict Greater Post-Deployment Mental Health Difficulties in U.S. Soldiers: A Cross-Lagged Analysis." Sleep 43, Supplement_1 (April 2020): A104. http://dx.doi.org/10.1093/sleep/zsaa056.271.

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Abstract Introduction Military service is associated with a number of occupational stressors, including non-conducive sleeping environments, shift schedules, and extended deployments overseas. Service members who undergo combat deployments are at increased risk for mental health and sleep difficulties. Bidirectional associations between sleep and mental health difficulties are routinely observed, but the directional association of these difficulties from one deployment to the next has not been addressed. The purpose of this study was to examine whether residual sleep problems or mental health difficulties after a 12-month period of reset operations following an initial deployment were associated with changes in sleep and mental health following a subsequent deployment. Methods Data from 74 U.S. Soldiers were case-matched across three time points. Participants were assessed 6 months (T1) and 12 months (T2) following an initial deployment. Participants were then assessed 3 months (T3) following a subsequent deployment. Symptoms of PTSD, anxiety, depression, and sleep difficulties were assessed at all three time points. Results Cross-lagged hierarchical regression models revealed that residual sleep difficulties across the time points uniquely predicted later changes in PTSD and anxiety symptoms, but not depressive symptoms, following a subsequent deployment. Conversely, residual mental health difficulties were not unique predictors of later changes in sleep difficulties. Conclusion These findings suggest that higher levels of residual sleep difficulties 12 months following a prior deployment are associated with larger increases in mental health problems following a subsequent deployment. Moreover, and importantly, the converse association was not supported. Residual mental health difficulties prior to deployment were not associated with changes in sleep difficulties. These data provide a viable target for intervention during reset operations to mitigate mental health difficulties associated with combat deployments. They might also help inform return-to-duty decisions. Support N/A.

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Wang, Zhigang, Liqin Tian, Lianhai Lin, and Yinghua Tong. "Lattice-Based 3-Dimensional Wireless Sensor Deployment." Journal of Sensors 2021 (August 17, 2021): 1–14. http://dx.doi.org/10.1155/2021/2441122.

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With the wide application of wireless sensor networks (WSNs) in real space, there are numerous studies on 3D sensor deployments. In this paper, the k -connectivity theoretical model of fixed and random nodes in regular lattice-based deployment was proposed to study the coverage and connectivity of sensor networks with regular lattice in 3D space. The full connectivity range and cost of the deployment with sensor nodes fixed in the centers of four regular lattices were quantitatively analyzed. The optimal single lattice coverage model and the ratio of the communication range to the sensing range r c / r s were investigated when the deployment of random nodes satisfied the k -connectivity requirements for full coverage. In addition, based on the actual sensing model, the coverage, communication link quality, and reliability of different lattice-based deployment models were determined in this study.

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Esposito, Richard A., Larry S. Monroe, and Julio S. Friedman. "Deployment Models for Commercialized Carbon Capture and Storage†." Environmental Science & Technology 45, no. 1 (January 2011): 139–46. http://dx.doi.org/10.1021/es101441a.

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Kaskie, B. "The Widespread Deployment of Integrated Models of Care." Public Policy & Aging Report 23, no. 3 (June 1, 2013): 1–9. http://dx.doi.org/10.1093/ppar/23.3.1a.

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Boubrima, Ahmed, Walid Bechkit, and Herve Rivano. "Optimal WSN Deployment Models for Air Pollution Monitoring." IEEE Transactions on Wireless Communications 16, no. 5 (May 2017): 2723–35. http://dx.doi.org/10.1109/twc.2017.2658601.

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Ng, ManWo. "Revisiting a class of liner fleet deployment models." European Journal of Operational Research 257, no. 3 (March 2017): 773–76. http://dx.doi.org/10.1016/j.ejor.2016.07.044.

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Reingle Gonzalez, Jennifer M., Stephen A. Bishopp, Katelyn K. Jetelina, Ellen Paddock, Kelley Pettee Gabriel, and M. Brad Cannell. "Does military veteran status and deployment history impact officer involved shootings? A case–control study." Journal of Public Health 41, no. 3 (October 3, 2018): e245-e252. http://dx.doi.org/10.1093/pubmed/fdy151.

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AbstractBackgroundDespite veterans’ preference hiring policies by law enforcement agencies, no studies have examined the nature or effects of military service or deployments on health outcomes. This study will examine the effect of military veteran status and deployment history on law enforcement officer (LEO)-involved shootings.MethodsTen years of data were extracted from Dallas Police Department records. LEOs who were involved in a shooting in the past 10 years were frequency matched on sex to LEOs never involved in a shooting. Military discharge records were examined to quantify veteran status and deployment(s). Multivariable logistic regression was used to estimate the effect of veteran status and deployment history on officer-involved shooting involvement.ResultsRecords were abstracted for 516 officers. In the adjusted models, veteran LEOs who were not deployed were significantly more likely to be involved in a shooting than non-veteran officers. Veterans with a deployment history were 2.9 times more likely to be in a shooting than non-veteran officers.ConclusionsMilitary veteran status, regardless of deployment history, is associated with increased odds of shootings among LEOs. Future studies should identify mechanisms that explain this relationship, and whether officers who experienced firsthand combat exposure experience greater odds of shooting involvement.

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Nissen, Lars R., Karen-Inge Karstoft, Mia S. Vedtofte, Anni B. S. Nielsen, Merete Osler, Erik L. Mortensen, Gunhild T. Christensen, and Søren B. Andersen. "Cognitive ability and risk of post-traumatic stress disorder after military deployment: an observational cohort study." BJPsych Open 3, no. 6 (November 2017): 274–80. http://dx.doi.org/10.1192/bjpo.bp.117.005736.

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BackgroundStudies of the association between pre-deployment cognitive ability and post-deployment post-traumatic stress disorder (PTSD) have shown mixed results.AimsTo study the inflence of pre-deployment cognitive ability on PTSD symptoms 6–8 months post-deployment in a large population while controlling for pre-deployment education and deployment-related variables.MethodStudy linking prospective pre-deployment conscription board data with post-deployment self-reported data in 9695 Danish Army personnel deployed to different war zones in 1997–2013. The association between pre-deployment cognitive ability and post-deployment PTSD was investigated using repeated-measure logistic regression models. Two models with cognitive ability score as the main exposure variable were created (model 1 and model 2). Model 1 was only adjusted for pre-deployment variables, while model 2 was adjusted for both pre-deployment and deployment-related variables.ResultsWhen including only variables recorded pre-deployment (cognitive ability score and educational level) and gender (model 1), all variables predicted post-deployment PTSD. When deployment-related variables were added (model 2), this was no longer the case for cognitive ability score. However, when educational level was removed from the model adjusted for deployment-related variables, the association between cognitive ability and post-deployment PTSD became significant.ConclusionsPre-deployment lower cognitive ability did not predict post-deployment PTSD independently of educational level after adjustment for deployment-related variables.

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Dunbar, Christopher R., Mark S. Riddle, Kristen Clarkson, Ramiro L. Gutierrez, Ashley Alcala, Angelique Byrd, and Chad K. Porter. "1104. Deployment-Associated Infectious Gastroenteritis and Associations With Irritable Bowel Syndrome, Post-Traumatic Stress Disorder, and Combat Stress: A Retrospective Cohort Study Among Deployed United States Military Personnel." Open Forum Infectious Diseases 5, suppl_1 (November 2018): S331. http://dx.doi.org/10.1093/ofid/ofy210.938.

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Abstract Background Previous studies have shown an association between post-traumatic stress disorder (PTSD) and the development of irritable bowel syndrome (IBS) in deployed service members. Deployment places soldiers at risk for chemical, physical, psychological, and infectious stressors. Acute stress can alter the gastrointestinal barrier leading to gut barrier dysfunction, which is an independent risk factor for infectious gastroenteritis (IGE). We sought to assess if there was an association between IBS and PTSD in military deployed in support of recent and ongoing military operations. Methods We conducted a retrospective cohort study of United States service members who participated in a combat deployment to the Middle East from 2001 to 2013 with no prior Axis I disorders or PTSD diagnoses based on data from the Defense Medical Surveillance System. Univariate and multivariate logistic regression models were used to assess the differential risk of PTSD following a combat deployment among those with and without a predeployment diagnosis of IBS. These models were controlled for confounders/covariates of interest (IGE, age, duration of deployment, sex, race, marital status, education level, military rank, branch of service, number of deployments). Results Among the 3825 subjects, those who developed IGE had a 34% (P = 0.02) increased risk of PTSD compared with those with no IGE during deployment. Additionally, those with IBS predeployment had a 40% (P = 0.001) increased risk of PTSD upon return from deployment compared with those without IBS predeployment. Duration of deployment was significantly (P < 0.0001) associated with PTSD with an increasing risk with increasing duration of deployment. Conclusion IGE and IBS were significantly associated with PTSD further supporting previous studies describing their association. Baseline chronic dysbiosis and acute stress-related microbiota perturbations may lead to short- and long-term resilience and performance deficits in our soldiers that may compromise mission capabilities and decrease the quality of life in returning soldiers. Further understanding the potential interactions between the gut–brain–microbiome may have immediate and long-term impacts on improving warfighter health and performance. Disclosures All authors: No reported disclosures.

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Fan, Tie Gang, Gui Fa Teng, and Li Min Huo. "Minimum Cost Programming Models of Nodes Deployment for WSNs." Applied Mechanics and Materials 719-720 (January 2015): 696–701. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.696.

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WSNs can cover a wide range of application. Node deployment is a fundamental factor in determining the connectivity, coverage, lifetime and cost of WSNs. This paper focuses on the cost of network that satisfies some constraints (coverage, connectivity and lifetime). In order to satisfy the connectivity and coverage, we use the regular hexagonal cell architecture. We present a new metric, the Cost Per Unit Area and Lifetime, to be objective function. Three programming models are proposed under different scenarios. For reasons of space, we present briefly the method to solve above models and some analysis.

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Restrepo, Mateo, Shane G. Henderson, and Huseyin Topaloglu. "Erlang loss models for the static deployment of ambulances." Health Care Management Science 12, no. 1 (July 27, 2008): 67–79. http://dx.doi.org/10.1007/s10729-008-9077-4.

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Wang, Tian, Weijia Jia, Guoliang Xing, and Minming Li. "Exploiting Statistical Mobility Models for Efficient Wi-Fi Deployment." IEEE Transactions on Vehicular Technology 62, no. 1 (January 2013): 360–73. http://dx.doi.org/10.1109/tvt.2012.2217159.

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Yan, Shangyao, Chung-Cheng Lu, and Min-Hung Wang. "Stochastic fleet deployment models for public bicycle rental systems." International Journal of Sustainable Transportation 12, no. 1 (June 1, 2017): 39–52. http://dx.doi.org/10.1080/15568318.2017.1324586.

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Castro, Diogo, Prasanth Kothuri, Piotr Mrowczynski, Danilo Piparo, and Enric Tejedor. "Apache Spark usage and deployment models for scientific computing." EPJ Web of Conferences 214 (2019): 07020. http://dx.doi.org/10.1051/epjconf/201921407020.

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This talk is about sharing our recent experiences in providing data analytics platform based on Apache Spark for High Energy Physics, CERN accelerator logging system and infrastructure monitoring. The Hadoop Service has started to expand its user base for researchers who want to perform analysis with big data technologies. Among many frameworks, Apache Spark is currently getting the most traction from various user communities and new ways to deploy Spark such as Apache Mesos or Spark on Kubernetes have started to evolve rapidly. Meanwhile, notebook web applications such as Jupyter offer the ability to perform interactive data analytics and visualizations without the need to install additional software. CERN already provides a web platform, called SWAN (Service for Web-based ANalysis), where users can write and run their analyses in the form of notebooks, seamlessly accessing the data and software they need. The first part of the presentation talks about several recent integrations and optimizations to the Apache Spark computing platform to enable HEP data processing and CERN accelerator logging system analytics. The optimizations and integrations, include, but not limited to, access of kerberized resources, xrootd connector enabling remote access to EOS storage and integration with SWAN for interactive data analysis, thus forming a truly Unified Analytics Platform. The second part of the talk touches upon the evolution of the Apache Spark data analytics platform, particularly sharing the recent work done to run Spark on Kubernetes on the virtualized and container-based infrastructure in Openstack. This deployment model allows for elastic scaling of data analytics workloads enabling efficient, on-demand utilization of resources in private or public clouds.

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Sivasamy, K., C. Arumugam, S. R. Devadasan, R. Murugesh, and V. M. M. Thilak. "Advanced models of quality function deployment: a literature review." Quality & Quantity 50, no. 3 (May 5, 2015): 1399–414. http://dx.doi.org/10.1007/s11135-015-0212-2.

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Prieto, Iñaki, Jose Luis Izkara, and Rubén Béjar. "A continuous deployment-based approach for the collaborative creation, maintenance, testing and deployment of CityGML models." International Journal of Geographical Information Science 32, no. 2 (October 26, 2017): 282–301. http://dx.doi.org/10.1080/13658816.2017.1393543.

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Peeta, Srinivas, and Jeong W. Yu. "Behavior-based consistency-seeking models as deployment alternatives to dynamic traffic assignment models." Transportation Research Part C: Emerging Technologies 14, no. 2 (April 2006): 114–38. http://dx.doi.org/10.1016/j.trc.2006.05.008.

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Hamadeh, Sima, and Marie Marquis. "Linked models and theories: a tool for school nutrition policies deployment." British Food Journal 121, no. 4 (April 1, 2019): 975–90. http://dx.doi.org/10.1108/bfj-10-2017-0592.

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Purpose The purpose of this paper is to explore the pertinence of using an integrated conceptual framework based on several theories and models to guide interviews with adults and youth as a prelude to school nutrition policy (SNP) deployment. Design/methodology/approach Appropriate socio-behavioral and communication theories and models within a social marketing approach were used to build the integrated conceptual framework of this study. The target population consists of 115 multidisciplinary key stakeholders in Lebanon. Directed and semi-structured individual interviews and focus groups were conducted by using questionnaires associated with the variables of the framework. Collected data have been submitted to a thematic qualitative analysis. Findings Combining theories and models increases the potential for understanding the broader determinants of SNP deployment. It is important to choose a holistic theoretical perspective: to study key stakeholders’ perceptions of the facilitators and barriers of SNP development and implementation, to emphasize the active participation of communities and to guide the work of policy and decision makers. Practical implications This research offers perspectives on determinants factors envisaged in the deployment of SNP that help key stakeholders in their promotion and communication practices. Social implications For public policy makers, this research suggests a need to address communities perceptions’ of an eventual SNP deployment. Originality/value The comprehensive integrated conceptual framework proposed in this study amalgamates several variables involved in the process of health promotion under various categories to facilitate SNP deployment.

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Levä, Tapio, Antti Riikonen, Juuso Töyli, and Heikki Hämmäinen. "A Framework for Measuring the Deployment of Internet Protocols." International Journal of IT Standards and Standardization Research 12, no. 1 (January 2014): 38–62. http://dx.doi.org/10.4018/ijitsr.2014010103.

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Internet protocols spread to potential adopters through several successive phases of implementation, commercialization, acquisition, and adoption of the protocol. This process of protocol deployment involves several stakeholders and varies depending on the deployment environment and the protocol in question. This complexity and the lack of comprehensive measurement studies call for a further conceptualization of measuring protocol diffusion along the whole deployment process. Therefore, this article develops a framework for measuring the deployment of Internet protocols, consisting of deployment steps, deployment models, deployment measures, and data sources. The measures are further linked to each other through deployment gaps and delays. In order to demonstrate the framework, it is used to assess how a set of pre-installed protocols spread in the Finnish mobile market. The framework highlights the differences between the deployment models and the importance to use both the deployment measures and gaps in the analysis of protocol success. Furthermore, the illustrative results indicate that protocol deployment is driven by applications, and show the existence of large deployment gaps between the protocol possession and usage. The results are relevant especially to researchers interested in holistically analyzing protocol deployment and protocol developers for measuring and improving the success of their protocols.

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Turner, Matthew, Yuan Liao, and Yan Du. "Comprehensive Smart Grid Planning in a Regulated Utility Environment." International Journal of Emerging Electric Power Systems 16, no. 3 (June 1, 2015): 265–79. http://dx.doi.org/10.1515/ijeeps-2014-0099.

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Abstract This paper presents the tools and exercises used during the Kentucky Smart Grid Roadmap Initiative in a collaborative electric grid planning process involving state regulators, public utilities, academic institutions, and private interest groups. The mandate of the initiative was to assess the existing condition of smart grid deployments in Kentucky, to enhance understanding of smart grid concepts by stakeholders, and to develop a roadmap for the deployment of smart grid technologies by the jurisdictional utilities of Kentucky. Through involvement of many important stakeholder groups, the resultant Smart Grid Deployment Roadmap proposes an aggressive yet achievable strategy and timetable designed to promote enhanced availability, security, efficiency, reliability, affordability, sustainability and safety of the electricity supply throughout the state while maintaining Kentucky’s nationally competitive electricity rates. The models and methods developed for this exercise can be utilized as a systematic process for the planning of coordinated smart grid deployments.

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Nicosia, Nancy, Elizabeth Wong, Victoria Shier, Samira Massachi, and Ashlesha Datar. "Parental Deployment, Adolescent Academic and Social–Behavioral Maladjustment, and Parental Psychological Well-being in Military Families." Public Health Reports 132, no. 1 (December 9, 2016): 93–105. http://dx.doi.org/10.1177/0033354916679995.

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Objective: Increases in the frequency and length of military deployments have raised concerns about the well-being of military families. We examined the relationship between a military parent’s deployment and (1) adolescent academic and social–behavioral maladjustment and (2) parental psychological well-being. Methods: We collected data from April 2013 through January 2014 from 1021 families of enlisted US Army personnel with children aged 12 or 13 during the Military Teenagers’ Environments, Exercise, and Nutrition Study. Through online parent surveys, we collected data on deployment, adolescent academic and social–behavioral maladjustment, and parental psychological well-being. We estimated adjusted logistic and linear regression models for adolescents (all, boys, girls), military parents (all, fathers, mothers), and civilian parents. Results: Compared with no or short deployments, long deployments (>180 days in the past 3 years) were associated with significantly higher odds of decreases in adolescent academic performance (adjusted odds ratio [AOR] = 1.54), independence (AOR = 2.04), and being responsible (AOR = 1.95). These associations were also significant for boys but not for girls. Among parents, long deployments were associated with significantly higher odds of being depressed (AOR = 2.58), even when controlling for adolescent maladjustment (AOR = 2.54). These associations did not differ significantly between military and civilian parents and were significant for military fathers but not military mothers. Recent deployment (in the past 12 months) was not associated with either adolescent or parent outcomes. Conclusion: Long deployments are associated with adolescents’ academic and social–behavioral maladjustments and diminished parental well-being, especially among boys and military fathers.

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Fung, Han Ping. "A Tool to Map Applications to Information Technology Deployment Models." Revista de Administração de Roraima - RARR 6, no. 1 (August 2, 2016): 114. http://dx.doi.org/10.18227/2237-8057rarr.v6i1.3233.

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No momento em que a adoção de cloud computing e outsourcing de TI para gerenciamento de aplicações ainda é prevalente. No entanto, ainda há falta de pesquisa ou ferramenta para mapear uma aplicação para um modelo de implantação de TI específico, como nuvem pública, nuvem privada, outsourcing de TI ou a gestão in-house. Este problema de pesquisa tem incentivado esta pesquisa e com a revisão de literatura, desenvolveu-se uma ferramenta de mapeamento Microsoft Excel, bem como foi testado por 26 aplicações de duas organizações em Singapura. Esta ferramenta de mapeamento é baseado em 10 critérios para avaliar as características de cada aplicativo, antes de mapeá-los para o modelo de implantação de TI apropriada. Estes 10 critérios incluem: 1) Conformidade Regulamentar, 2) Apoio Vendedor, 3) Disponibilidade de serviço, 4) Workload Variability, 5) Segurança, 6) Infra-estrutura de compatibilidade, 7) Adequação Internet, 8) Arquitetura, 9) Finanças e 10) Afinidade e Integração da Intranet. Este artigo demonstra que a ferramenta de mapeamento é um ativo valioso tanto para a gestão de TI e consultoria de TI em aplicações de avaliação e mapeá-los para os modelos de implantação de TI corretos. Os benefícios do uso desta ferramenta de mapeamento incluem precisão, consistência e velocidade no mapeamento das aplicações para os modelos de implantação de TI adequadas.

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Daioglou, Vassilis, Steven K. Rose, Nico Bauer, Alban Kitous, Matteo Muratori, Fuminori Sano, Shinichiro Fujimori, et al. "Bioenergy technologies in long-run climate change mitigation: results from the EMF-33 study." Climatic Change 163, no. 3 (August 24, 2020): 1603–20. http://dx.doi.org/10.1007/s10584-020-02799-y.

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AbstractBioenergy is expected to play an important role in long-run climate change mitigation strategies as highlighted by many integrated assessment model (IAM) scenarios. These scenarios, however, also show a very wide range of results, with uncertainty about bioenergy conversion technology deployment and biomass feedstock supply. To date, the underlying differences in model assumptions and parameters for the range of results have not been conveyed. Here we explore the models and results of the 33rd study of the Stanford Energy Modeling Forum to elucidate and explore bioenergy technology specifications and constraints that underlie projected bioenergy outcomes. We first develop and report consistent bioenergy technology characterizations and modeling details. We evaluate the bioenergy technology specifications through a series of analyses—comparison with the literature, model intercomparison, and an assessment of bioenergy technology projected deployments. We find that bioenergy technology coverage and characterization varies substantially across models, spanning different conversion routes, carbon capture and storage opportunities, and technology deployment constraints. Still, the range of technology specification assumptions is largely in line with bottom-up engineering estimates. We then find that variation in bioenergy deployment across models cannot be understood from technology costs alone. Important additional determinants include biomass feedstock costs, the availability and costs of alternative mitigation options in and across end-uses, the availability of carbon dioxide removal possibilities, the speed with which large scale changes in the makeup of energy conversion facilities and integration can take place, and the relative demand for different energy services.

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Diaby, Tinankoria, and Babak Bashari Rad. "Cloud Computing: A review of the Concepts and Deployment Models." International Journal of Information Technology and Computer Science 9, no. 6 (June 8, 2017): 50–58. http://dx.doi.org/10.5815/ijitcs.2017.06.07.

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Noel, Jeffrey K., Paul C. Whitford, Karissa Y. Sanbonmatsu, and Josï¿½ N. Onuchic. "SMOG@ctbp: simplified deployment of structure-based models in GROMACS." Nucleic Acids Research 38, suppl_2 (June 4, 2010): W657—W661. http://dx.doi.org/10.1093/nar/gkq498.

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Mahmud, S. A., G. M. Khan, H. Zafar, K. Ahmad, and N. Behttani. "A Survey on Femtocells: Benefits Deployment Models and Proposed Solutions." Journal of Applied Research and Technology 11, no. 5 (October 2013): 733–54. http://dx.doi.org/10.1016/s1665-6423(13)71582-7.

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Liu, Xinxin, Heng-Qing Ye, and Xue-Ming Yuan. "Tactical planning models for managing container flow and ship deployment." Maritime Policy & Management 38, no. 5 (September 2011): 487–508. http://dx.doi.org/10.1080/03088839.2011.597447.

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Lameijer, Bart A., Jeroen De Mast, and Ronald J. M. M. Does. "Lean Six Sigma Deployment and Maturity Models: A Critical Review." Quality Management Journal 24, no. 4 (January 1, 2017): 6–20. http://dx.doi.org/10.1080/10686967.2017.12088376.

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Islam, Shareeful, Moussa Ouedraogo, Christos Kalloniatis, Haralambos Mouratidis, and Stefanos Gritzalis. "Assurance of Security and Privacy Requirements for Cloud Deployment Models." IEEE Transactions on Cloud Computing 6, no. 2 (April 1, 2018): 387–400. http://dx.doi.org/10.1109/tcc.2015.2511719.

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Zhao, Gansen, Chunming Rong, Martin Gilje Jaatun, and Frode Eika Sandnes. "Reference deployment models for eliminating user concerns on cloud security." Journal of Supercomputing 61, no. 2 (June 17, 2010): 337–52. http://dx.doi.org/10.1007/s11227-010-0460-9.

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de Prado, Miguel, Manuele Rusci, Alessandro Capotondi, Romain Donze, Luca Benini, and Nuria Pazos. "Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles." Sensors 21, no. 4 (February 13, 2021): 1339. http://dx.doi.org/10.3390/s21041339.

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Standard-sized autonomous vehicles have rapidly improved thanks to the breakthroughs of deep learning. However, scaling autonomous driving to mini-vehicles poses several challenges due to their limited on-board storage and computing capabilities. Moreover, autonomous systems lack robustness when deployed in dynamic environments where the underlying distribution is different from the distribution learned during training. To address these challenges, we propose a closed-loop learning flow for autonomous driving mini-vehicles that includes the target deployment environment in-the-loop. We leverage a family of compact and high-throughput tinyCNNs to control the mini-vehicle that learn by imitating a computer vision algorithm, i.e., the expert, in the target environment. Thus, the tinyCNNs, having only access to an on-board fast-rate linear camera, gain robustness to lighting conditions and improve over time. Moreover, we introduce an online predictor that can choose between different tinyCNN models at runtime—trading accuracy and latency—which minimises the inference’s energy consumption by up to 3.2×. Finally, we leverage GAP8, a parallel ultra-low-power RISC-V-based micro-controller unit (MCU), to meet the real-time inference requirements. When running the family of tinyCNNs, our solution running on GAP8 outperforms any other implementation on the STM32L4 and NXP k64f (traditional single-core MCUs), reducing the latency by over 13× and the energy consumption by 92%.

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Ahn, Hyun, and Kwanghoon Pio Kim. "Formal Approach to Workflow Application Fragmentations Over Cloud Deployment Models." Computers, Materials & Continua 67, no. 3 (2021): 3071–88. http://dx.doi.org/10.32604/cmc.2021.015280.

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Karstoft, Karen-Inge, Ioannis Tsamardinos, Kasper Eskelund, Søren Bo Andersen, and Lars Ravnborg Nissen. "Applicability of an Automated Model and Parameter Selection in the Prediction of Screening-Level PTSD in Danish Soldiers Following Deployment: Development Study of Transferable Predictive Models Using Automated Machine Learning." JMIR Medical Informatics 8, no. 7 (July 22, 2020): e17119. http://dx.doi.org/10.2196/17119.

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Background Posttraumatic stress disorder (PTSD) is a relatively common consequence of deployment to war zones. Early postdeployment screening with the aim of identifying those at risk for PTSD in the years following deployment will help deliver interventions to those in need but have so far proved unsuccessful. Objective This study aimed to test the applicability of automated model selection and the ability of automated machine learning prediction models to transfer across cohorts and predict screening-level PTSD 2.5 years and 6.5 years after deployment. Methods Automated machine learning was applied to data routinely collected 6-8 months after return from deployment from 3 different cohorts of Danish soldiers deployed to Afghanistan in 2009 (cohort 1, N=287 or N=261 depending on the timing of the outcome assessment), 2010 (cohort 2, N=352), and 2013 (cohort 3, N=232). Results Models transferred well between cohorts. For screening-level PTSD 2.5 and 6.5 years after deployment, random forest models provided the highest accuracy as measured by area under the receiver operating characteristic curve (AUC): 2.5 years, AUC=0.77, 95% CI 0.71-0.83; 6.5 years, AUC=0.78, 95% CI 0.73-0.83. Linear models performed equally well. Military rank, hyperarousal symptoms, and total level of PTSD symptoms were highly predictive. Conclusions Automated machine learning provided validated models that can be readily implemented in future deployment cohorts in the Danish Defense with the aim of targeting postdeployment support interventions to those at highest risk for developing PTSD, provided the cohorts are deployed on similar missions.

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Sowden, Walter, Alexxa Bessey, Julie Merrill, Ashlee Mckeon, Jake Choynowski, and Janna Mantua. "304 The relationship between basic personality, subjective sleep duration, and psychological distress during a military deployment." Sleep 44, Supplement_2 (May 1, 2021): A121. http://dx.doi.org/10.1093/sleep/zsab072.303.

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Abstract Introduction Extended, overseas operations (deployments) increase the likelihood that military personnel will experience psychological distress. Reduced sleep during deployments is a key correlate of psychological distress. Thus, it is imperative to identify mechanisms that adaptively modulate the relationship between insufficient sleep and psychological distress. Research has recently connected basic personality traits (i.e., the Big Five: extraversion, agreeableness, conscientiousness, neuroticism, and openness) to more sleep. The current project aimed to examine the relationship between basic personality traits, subjective sleep duration (SSD), and psychological distress during an operational deployment. Methods 488 soldiers took surveys both prior to and half-way through a nine-month deployment. The pre-deployment survey included the Big Five Index, and three standardized measures of psychological distress commonly used to screen military personnel for anxiety (Generalized Anxiety Disorder; GAD-7), depression (Patient Health Questionnaire; PHQ-8), and post-traumatic stress (Posttraumatic Symptom Disorder Checklist; PCL-4). The mid-deployment survey included an item from the Pittsburgh Sleep Quality Index measuring SSD and the same psychological distress measures from the pre-deployment survey. General linear models were used to test the interaction between SSD and each basic personality trait on each measure of psychological distress at mid-deployment while accounting for psychological distress at pre-deployment. Results Of the Big Five, conscientiousness was the only trait to significantly moderate the relationship between SSD and anxiety, t = 2.11, p = .035, where higher conscientiousness weakened the relationship. Further only agreeableness attenuated the relationship between depression and SSD, t = 2.10, p = .036. Interestingly, the only Big Five trait that moderated the relationship between SSD and PTS was openness, insomuch that openness strengthened the relationship, t = -1.92, p = .055. Conclusion The relationship between SSD and psychological distress was uniquely impacted by different personality traits. These results reinforce the age-old concept that behavior is the product of a complex, nuanced, and puzzling interaction between the individual and the environment. The current research motivates further research into personality as an adaptive mechanism for optimizing military wellbeing. Support (if any) Support for this study came from the Military Operational Medicine Research Program (MOMRP) of the United States Army Medical Research and Development Command (USAMRDC).

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Ibrahim, Shewkar, and Tarek Sayed. "Using Bayesian Tobit Models to Understand the Impact of Mobile Automated Enforcement on Collision and Crime Rates." Sustainability 13, no. 11 (June 5, 2021): 6422. http://dx.doi.org/10.3390/su13116422.

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The Data Driven Approaches to Crime and Traffic Safety approach identifies opportunities where a single enforcement deployment can achieve multiple objectives: reduce collision and crime rates. Previous research focused on modeling both events separately despite evidence suggesting a high correlation. Additionally, there is a limited understanding of the impact of Mobile Automated Enforcement (MAE) on crime or the impact of changing a deployment strategy on collision and crime dates. For this reason, this study categorized MAE deployment into three different clusters. A random-parameter multivariate Tobit model was developed under the Bayesian framework to understand the impact of changing the deployment on collision and crime rates in a neighborhood. Firstly, the results of the analysis quantified the high correlation between collision and crime rates (0.86) which suggest that locations with high collision rates also coincide with locations with high crime rates. The results also demonstrated the safety effectiveness (i.e., reduced crime and collision rates) increased for the clusters that are associated with an increased enforcement duration at a neighborhood level. Understanding how changing the deployment strategy at a macro-level affects collision and crime rates provides enforcement agencies with the opportunity to maximize the efficiency of their existing resources.

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Krishnamurthi, Rajalakshmi, Raghav Maheshwari, and Rishabh Gulati. "Colorizing and Captioning Images Using Deep Learning Models and Deploying Them Via IoT Deployment Tools." International Journal of Information Retrieval Research 10, no. 4 (October 2020): 35–50. http://dx.doi.org/10.4018/ijirr.2020100103.

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Neural networks and IoT are some top fields of research in computer science nowadays. Inspired by this, this article works on using and creating an efficient neural networks model for colorizing images and transports them to remote systems through IoT deployment tools. This article develops two models, Alpha and Beta, for the colorization of the greyscale images. Efficient models are developed to lessen the loss rate to around 0.005. Further, it also develops an efficient model for the captioning of an image. The paper then describes the use of tools like AWS Greengrass and Docker for the deployment of different neural networks models, providing a comparative analysis among them, combining neural networks with IoT deployment tools.

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Crawford, Sean A., Matthew G. Doyle, Cristina H. Amon, and Thomas L. Forbes. "Impact of Insertion Technique and Iliac Artery Anatomy on Fenestrated Endovascular Aneurysm Repair." Journal of Endovascular Therapy 26, no. 6 (August 28, 2019): 797–804. http://dx.doi.org/10.1177/1526602819872499.

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Purpose: To develop a mechanically realistic aortoiliac model to evaluate anatomic variables associated with stent-graft rotation and to assess common deployment techniques that may contribute to rotation. Materials and Methods: Idealized aortoiliac geometries were constructed either through direct 3-dimensional (3D) printing (rigid) or through casting with polyvinyl alcohol using 3D-printed molds (flexible). Flexible model bending rigidity was controlled by altering wall thickness. Three flexible patient-specific models were also created based on the preoperative computed tomography angiograms. Zenith infrarenal and fenestrated devices were used in this study. The models were pressurized to 100 mm Hg with normal saline. Deployments were performed under fluoroscopy at 37°C. Rotation was calculated by tracking the change in position of gold markers affixed to the devices. Results: In the rigid idealized models, stent-graft rotation increased with increasing torsion; torsion levels of 1.6, 2.6, and 3.6 mm−1 had mean rotations of 5.2°±0.03°, 11.2°±4.8°, and 27.6°±13.0°, respectively (p<0.001). In the flexible models, the highest rotation (58°±3.0°) was observed in models with high torsion and high rigidity (7.5 mm−1 net torsion and 254 N·m2 flexural rigidity). No rotation was observed in the absence of torsion. Applying torque to the device during insertion significantly increased stent-graft rotation by an average of 28° across all levels of torsion (p<0.01). Multiple device insertions prior to deployment did not change the observed device rotation. The patient-specific models accurately predicted the degree of rotation seen intraoperatively to within 5°. Conclusion: Insertion technique plays an important role in the degree of stent-graft rotation during deployment. Our model suggests that in vivo correction of device orientation can increase the observed rotation and supports the concept of fully removing the device, adjusting the orientation, and subsequently reinserting. Additionally, increasing iliac artery torsion in the presence of increased vessel rigidity results in stent-graft rotation.

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Hieder, Inaam Abbas. "Compared to wireless deployment in areas with different environmentse." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (April 1, 2019): 934. http://dx.doi.org/10.11591/ijece.v9i2.pp934-940.

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<p>In the mobile phone system, it is highly desirable to estimate the loss of the track not only to improve performance but also to achieve an accurate estimate of financial feasibility; the inaccurate estimate of track loss either leads to performance degradation or increased cost. Various models have been introduced to accurately estimate the path loss. One of these models is the Okomura / Hata model, which is recommended for estimating path loss in cellular systems that use micro cells. This system is suitable for use in a variety of environments. This study examines the comparison of path loss models for statistical analysis derived from experimental data collected in urban and suburban areas at frequencies of 150-1500 MHz’s The results of the measurements were used to develop path loss models in urban and suburban areas. The results showed that Pathloss increases in urban areas respectively.</p>

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Lei, Ruibo, Zhijun Li, Yanfeng Cheng, Xin Wang, and Yao Chen. "A New Apparatus for Monitoring Sea Ice Thickness Based on the Magnetostrictive-Delay-Line Principle." Journal of Atmospheric and Oceanic Technology 26, no. 4 (April 1, 2009): 818–27. http://dx.doi.org/10.1175/2008jtecho613.1.

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Abstract High-precision ice thickness observations are required to gain a better understanding of ocean–ice–atmosphere interactions and to validate numerical sea ice models. A new apparatus for monitoring sea ice and snow thickness has been developed, based on the magnetostrictive-delay-line (MDL) principle for positioning sensors. This system is suited for monitoring fixed measurement sites on undeformed ice. The apparatus presented herein has been tested on landfast ice near Zhongshan Station, East Antarctica, for about 6 months during the austral autumn and winter of 2006; valid data records from the deployment are available for more than 90% of the deployment’s duration. The apparatus’s precision has been estimated to be ±0.002 m for the deployment. Therefore, it is possible that this apparatus may become a standard for sea ice/snow thickness monitoring.

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Journal articles on the topic 'Deployment models'

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