scholarly journals Meeting report of the fourth annual Tri-Service Microbiome Consortium symposium

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Michael S. Goodson ◽  
Robyn A. Barbato ◽  
J. Philip Karl ◽  
Karl Indest ◽  
Nancy Kelley-Loughnane ◽  
...  
Keyword(s):  
Information Sharing ◽  
Department Of Defense ◽  
Collaborative Research ◽  
Meeting Report ◽  
Annual Symposium ◽  
Research Opportunities ◽  
Microbiome Analysis ◽  
Enabling Technologies ◽  
Microbiome Research ◽  
And Performance

AbstractThe Tri-Service Microbiome Consortium (TSMC) was founded to enhance collaboration, coordination, and communication of microbiome research among U.S. Department of Defense (DoD) organizations. The annual TSMC symposium is designed to enable information sharing between DoD scientists and leaders in the field of microbiome science, thereby keeping DoD consortium members informed of the latest advances within the microbiome community and facilitating the development of new collaborative research opportunities. The 2020 annual symposium was held virtually on 24–25 September 2020. Presentations and discussions centered on microbiome-related topics within four broad thematic areas: (1) Enabling Technologies; (2) Microbiome for Health and Performance; (3) Environmental Microbiome; and (4) Microbiome Analysis and Discovery. This report summarizes the presentations and outcomes of the 4th annual TSMC symposium.

scholarly journals The Current and Future State of Department of Defense (DoD) Microbiome Research: a Summary of the Inaugural DoD Tri-Service Microbiome Consortium Informational Meeting

mSystems ◽  
2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Sarah Glaven ◽  
Kenneth Racicot ◽  
Dagmar H. Leary ◽  
J. Philip Karl ◽  
Steven Arcidiacono ◽  
...  
Keyword(s):  
Human Health ◽  
Air Force ◽  
Department Of Defense ◽  
Collaborative Research ◽  
Research Policy ◽  
Future Directions ◽  
Research Activities ◽  
Future State ◽  
Microbiome Research ◽  
And Performance

ABSTRACT The Tri-Service Microbiome Consortium (TSMC) was recently established to enhance collaboration, coordination, and communication of microbiome research among Department of Defense (DoD) organizations. The TSMC aims to serve as a forum for sharing information related to DoD microbiome research, policy, and applications, to monitor global advances relevant to human health and performance, to identify priority objectives, and to facilitate Tri-Service (Army, Navy, and Air Force) collaborative research. The inaugural TSMC workshop held on 10 to 11 May 2017 brought together almost 100 attendees from across the DoD and several key DoD partners. The meeting outcomes informed attendees of the scope of current DoD microbiome research efforts and identified knowledge gaps, collaborative/leveraging opportunities, research barriers/challenges, and future directions. This report details meeting presentations and discussions with special emphasis on Tri-Service labs’ current research activities.

scholarly journals Impact of the Human Microbiome in Forensic Sciences: a Systematic Review

2020 ◽  
Vol 86 (22) ◽  
Author(s):  
Manuel G. García ◽  
María D. Pérez-Cárceles ◽  
Eduardo Osuna ◽  
Isabel Legaz
Keyword(s):  
Systematic Review ◽  
Forensic Science ◽  
Human Microbiome ◽  
Geographical Location ◽  
Postmortem Interval ◽  
Forensic Sciences ◽  
Content Type ◽  
Microbiome Analysis ◽  
Location Of Death ◽  
Microbiome Research

ABSTRACT Numerous studies relate differences in microbial communities to human health and disease; however, little is known about microbial changes that occur postmortem or the possible applications of microbiome analysis in the field of forensic science. The aim of this review was to study the microbiome and its applications in forensic sciences and to determine the main lines of investigation that are emerging, as well as its possible contributions to the forensic field. A systematic review of the human microbiome in relation to forensic science was carried out by following PRISMA guidelines. This study sheds light on the role of microbiome research in the postmortem interval during the process of decomposition, identifying death caused by drowning or sudden death, locating the geographical location of death, establishing a connection between the human microbiome and personal items, sexual contact, and the identification of individuals. Actinomycetaceae, Bacteroidaceae, Alcaligenaceae, and Bacilli play an important role in determining the postmortem interval. Aeromonas can be used to determine the cause of death, and Corynebacterium or Helicobacter pylori can be used to ascertain personal identity or geographical location. Several studies point to a promising future for microbiome analysis in the different fields of forensic science, opening up an important new area of research.

scholarly journals Simulation Research on Information-Sharing Value of Two-Level Supply Chain

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaheng Zhang ◽  
Zekai Lin ◽  
Lin Xiao
Keyword(s):  
Supply Chain ◽  
Performance Evaluation ◽  
Information Sharing ◽  
Chain Model ◽  
Simulation Research ◽  
Incentive Effect ◽  
Actual Evaluation ◽  
Demand Information ◽  
And Performance ◽  
The Impact

In the two-stage supply chain model, the incentive effect to the supplier’s sharing of demand information and performance evaluation and the effect of various parameters on the incentive effect of the supply chain are studied through a multiagent simulation model constructed for the purpose. It is found that the incentive coefficient of demand information-sharing degree, the number of selected suppliers, the order allocation coefficient, and the order proportion are positively related to the incentive effect of demand information sharing. So, the greater the demand information sharing is, the greater the impact of these parameters on the incentive effect is. Based on the demand information sharing, the supplier performance evaluation rules are shared, and when the actual evaluation rules are inconsistent with the supplier’s expectations, the incentive effect is further enhanced. Other parameters do not affect the incentive effect of demand information sharing and performance evaluation rule sharing.

Social capital, relational learning, and performance of suppliers

2018 ◽  
Vol 30 (2) ◽  
pp. 417-437 ◽  
Author(s):  
Badri Munir Sukoco ◽  
Hardi Hardi ◽  
Alfiyatul Qomariyah
Keyword(s):  
Social Capital ◽  
Supply Chain ◽  
Information Sharing ◽  
Knowledge Integration ◽  
Relational Learning ◽  
Social Practices ◽  
Sense Making ◽  
Content Type ◽  
And Performance ◽  
The Relationship

Purpose The relationship between buyers and suppliers over the years – social practices – facilitate the development of social capital (SC), and it contributes to the relationship performance (RP) for both parties. The purpose of this paper is to examine the mechanisms that transform SC into RP. By exercising the relationship learning (joint sense-making, information sharing, and knowledge integration), this paper proposes that SC will transform into RP. Design/methodology/approach Quantitative study was employed in this study. Questionnaires were distributed to first-tier supplier of Astra Group (Astra International) in Indonesia. In total, 211 questionnaires were used for data analysis in this study. Findings The results exhibit that cognitive and structural SC contribute to the development of relational SC. Further, relational SC was positively associated with joint sense-making, which then goes through information sharing, knowledge integration, and finally RP. Research limitations/implications The cross-sectional data in a specific context (a firm) in Indonesia serve as a major limitation of this study. The development of SC and learning as a social process might not be captured well by using the current method – surveys. Furthermore, a major problem is caused by a one-sided survey that depends on the suppliers’ perceptions and judgments of relationship learning and performance. Practical implications The results suggest that managers and other relationship actors would benefit from the competency to develop practices and activities with suppliers regarding developing trust. The trust development is facilitated by having common understanding and interactions regularly, either by participating in formal and/or informal activities with suppliers. Building consensus – joint sense-making, between buyers and suppliers are crucial practices in relationship learning before knowledge sharing and knowledge integration practices are in place. And finally, managers should actively integrate this knowledge in order to increase their RP. Originality/value This study empirically tests the supply chain practice view as a new theoretical perspective in the supply chain management literature. It also extends the utilization of social practices – SC – since it is crucial in a buyer-supplier relationship. It also presents that relationship learning is a mechanism that could transform SC into RP, and thus bridge the SC and collaborative learning theory. Finally, this study indicates that inside relational learning, there are sequences of joint sense-making-information sharing-knowledge integration, before it moves on to RP.

Big data and performance measurement research: trends, evolution and future opportunities

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alberto Sardi ◽  
Enrico Sorano ◽  
Valter Cantino ◽  
Patrizia Garengo
Keyword(s):  
Big Data ◽  
Performance Measurement ◽  
Performance Measurement System ◽  
Future Research ◽  
Holistic View ◽  
Content Type ◽  
Digital Revolution ◽  
Research Opportunities ◽  
And Performance ◽  
Number Of Publications

Purpose Current literature recognised big data as a digital revolution affecting all organisational processes. To obtain a competitive advantage from the use of big data, an efficient integration in a performance measurement system (PMS) is needed, but it is still a “great challenge” in performance measurement research. This paper aims to review the big data and performance measurement studies to identify the publications’ trends and future research opportunities. Design/methodology/approach The authors reviewed 873 documents on big data and performance carrying out an extensive bibliometric analysis using two main techniques, i.e. performance analysis and science mapping. Findings Results point to a significant increase in the number of publications on big data and performance, highlighting a shortage of studies on business, management and accounting areas, and on how big data can improve performance measurement. Future research opportunities are identified. They regard the development of further research to explain how performance measurement field can effectively integrate big data into a PMS and describe the main themes related to big data in performance measurement literature. Originality/value This paper gives a holistic view of big data and performance measurement research through the inclusion of numerous contributions on different research streams. It also encourages further study for developing concrete tools.

Acceleration-Based Remaining Life Prognostics for Terrain-Loaded Components on an Army Ground Vehicle System

2009 ◽  
Vol 52 (2) ◽  
pp. 40-49 ◽  
Author(s):  
Richard Heine ◽  
Donald Barker
Keyword(s):  
Department Of Defense ◽  
Remaining Life ◽  
Ground Vehicle ◽  
Wheeled Vehicle ◽  
Vehicle System ◽  
Acceleration Sensors ◽  
And Performance ◽  
Critical Components ◽  
The Military ◽  
Wheeled Vehicles

Use of a health and usage monitoring system (HUMS) is one method the Department of Defense is investigating to meet conflicting cost and performance goals for Army wheeled vehicles. One area where a HUMS would be of great benefit is monitoring critical components vulnerable to terrain-induced fatigue. While strain is typically the desired input to a fatigue model, acceleration sensors are less susceptible to damage from the military ground vehicle environment and provide more reliable data over long periods of usage. The feasibility of using vibratory inputs from an accelerometer to make component fatigue predictions for a military wheeled vehicle system is explored in this study, and the use of limited subsets of data for algorithm training are evaluated. An example component is used to demonstrate that the proposed HUMS algorithms are appropriate and provide suitably accurate fatigue predictions.

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