Robust goal programming for multi-objective optimization of data-driven problems: A use case for the United States transportation command's liner rate setting problem

Waste discharged into the environment (air and water) needs to meet the expectation of environmental quality standards. One method that has been widely studied and to be used in maintaining water quality is a combination of the Moving Bed Biofilm Reactor (MBBR) method. With the Dimensions database, in the last ten years (2010-2020), it is known that there are 373 journals regarding MBBR for wastewater treatment. Furthermore, a bibliometric analysis was carried out with data from 2017 to 2020, using visualization from Vosviewer to determine the development of research on MBBR. Indonesia is in 10th place, contributing five articles on MBBR. Some other countries conducting the most research on MBBR include China, the United States, Iran, India, and Germany. The Journal that publishes the most research on MBBR is Bioresource Technology. The author who has done the most research on MBBR is Olav Vadstein, by the issuance of four articles. Meanwhile, the institutions that write the most research on MBBR are the Norwegian University of Science and Technology and the Technical University of Denmark. Another combination of data-driven systematic literature review (SLR) is needed to complement the bibliometric analysis of the MBBR study for wastewater treatment.

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Data-Driven Recommendations for Promoting Collaboration Among School Security Personnel and School Social Workers in the United States

International Journal of School Social Work ◽

10.4148/2161-4148.1037 ◽

2019 ◽

Vol 4 (1) ◽

Cited By ~ 1

Author(s):

Soohyoung Lee ◽

Matthew Cuellar

Keyword(s):

United States ◽

Social Workers ◽

The United States ◽

Data Driven ◽

School Social Workers ◽

School Security ◽

Security Personnel

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Data-driven identification of potential Zika virus vectors

10.1101/077966 ◽

2016 ◽

Cited By ~ 1

Author(s):

Michelle V. Evans ◽

Tad A. Dallas ◽

Barbara A. Han ◽

Courtney C. Murdock ◽

John M. Drake

Keyword(s):

United States ◽

Zika Virus ◽

Vector Competence ◽

Empirical Studies ◽

The United States ◽

Data Driven ◽

Health Concern ◽

Correct Identification ◽

Populations At Risk ◽

Emerging Virus

AbstractZika is an emerging virus whose rapid spread is of great public health concern. Knowledge about transmission remains incomplete, especially concerning potential transmission in geographic areas in which it has not yet been introduced. To identify unknown vectors of Zika, we developed a data-driven model linking vector species and the Zika virus via vector-virus trait combinations that confer a propensity toward associations in an ecological network connecting flaviviruses and their mosquito vectors. Our model predicts that thirty-five species may be able to transmit the virus, seven of which are found in the continental United States, including Culex quinquefasciatus and Cx. pipiens. We suggest that empirical studies prioritize these species to confirm predictions of vector competence, enabling the correct identification of populations at risk for transmission within the United States.

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