Technology Transition Performance of the U.S. Department of Defense Small Business Innovation Research Program

Acquisition Research Program Sponsored Report SeriesSponsored Acquisition Research & Technical ReportsSustainable public procurement plays an important role in addressing not only environmental but also economic and social issues through government acquisitions from technology-based small suppliers. In this context, the objective of this study is to better understand the holistic public procurement process by assessing the operational efficiency of technology-based small suppliers and associating the economic aspect of public procurement with the social aspect, such as women-owned businesses. To this end, we analyzed U.S. Department of Defense Small Busi-ness Innovation Research grantees by combining network data envelopment analysis with bootstrap truncated regression analysis. Drawing on the analysis results, we found that (1) there is heterogeneity in the performance of research and development, network building, and commercialization sub-processes, and (2) there is a positive relationship between the overall performance and women-owned small suppliers who excel particularly in network building. The former implies that small suppliers may have different expertise in the chain of public procurement; the latter suggests that woman entrepreneurs with a business network may be able to outperform their counterparts in the public procurement market.Approved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

Finding a Common Weight Vector of Data Envelopment Analysis Based upon Bargaining Game

Data Envelopment Analysis (DEA) is a mathematical programming method for measuring the relative efficiency of Decision Making Units (DMUs) by evaluating their outputs and inputs. In the history of DEA, the cross-efficiency of jth DMU is widely used as an efficiency measure of a given DMUo among researchers. The approach always utilizes weights related to inputs and outputs in the assessment. Unfortunately, the weights are not always uniquely determined in the cross-efficiency measurement because DEA always suffers from an occurrence of multiple solutions, so indicating an occurrence of multiple weights. To overcome such a difficulty, this paper proposes a new approach for determining a common weight vector of DEA based on bargaining game

The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

「コロナ制圧タスクフォース」COVID-19患者由来の血液細胞における遺伝子発現の網羅的解析 --重症度に応じた遺伝子発現の変化には、ヒトゲノム配列の個人差が影響する--. 京都大学プレスリリース. 2022-08-23.Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection

DOCK2 is involved in the host genetics and biology of severe COVID-19

「コロナ制圧タスクフォース」COVID-19疾患感受性遺伝子DOCK2の重症化機序を解明 --アジア最大のバイオレポジトリーでCOVID-19の治療標的を発見--. 京都大学プレスリリース. 2022-08-10.Identifying the host genetic factors underlying severe COVID-19 is an emerging challenge. Here we conducted a genome-wide association study (GWAS) involving 2, 393 cases of COVID-19 in a cohort of Japanese individuals collected during the initial waves of the pandemic, with 3, 289 unaffected controls. We identified a variant on chromosome 5 at 5q35 (rs60200309-A), close to the dedicator of cytokinesis 2 gene (DOCK2), which was associated with severe COVID-19 in patients less than 65 years of age. This risk allele was prevalent in East Asian individuals but rare in Europeans, highlighting the value of genome-wide association studies in non-European populations. RNA-sequencing analysis of 473 bulk peripheral blood samples identified decreased expression of DOCK2 associated with the risk allele in these younger patients. DOCK2 expression was suppressed in patients with severe cases of COVID-19. Single-cell RNA-sequencing analysis (n = 61 individuals) identified cell-type-specific downregulation of DOCK2 and a COVID-19-specific decreasing effect of the risk allele on DOCK2 expression in non-classical monocytes. Immunohistochemistry of lung specimens from patients with severe COVID-19 pneumonia showed suppressed DOCK2 expression. Moreover, inhibition of DOCK2 function with CPYPP increased the severity of pneumonia in a Syrian hamster model of SARS-CoV-2 infection, characterized by weight loss, lung oedema, enhanced viral loads, impaired macrophage recruitment and dysregulated type I interferon responses. We conclude that DOCK2 has an important role in the host immune response to SARS-CoV-2 infection and the development of severe COVID-19, and could be further explored as a potential biomarker and/or therapeutic target

Two-Stage, Dynamic Data Envelopment Analysis of Technology Transition Performance in the U.S. Defense Sector

Both the commercial world and Department of Defense (DoD) are challenged with system safety issues when dealing with Machine Learned (ML)/Artificial Intelligence (AI) deployed products. DoD has a more severe issue when deploying weapons that could unintentionally harm groups of people and property. Commercial manufacturers are motivated by profit, while DoD is motivated by defense readiness. Both are in a race and can suffer the consequences from focusing too much on the finish line. Establishing formal oversight ensures safe algorithm performance. This paper presents a measurement approach that scrutinizes the quality and quantity of training data used when developing ML/AI algorithms. Measuring quality and quantity of training data increases confidence in how the algorithm will perform in a "realistic" operational environment. Combining modality with measurements determines: (1) how to curate data to support a realistic deployed environment; (2) what attributes take priority during training to ensure robust composition of the data; and (3) how attribute prioritization is reflected in size of the training set. The measurements provide a greater understanding of the operational environment, taking into account issues that result when missing and/or sparse data occur, as well as how data sources supply input to the algorithm during deployment.Prepared for the Naval Postgraduate School, Monterey, CA 93943.Naval Postgraduate SchoolApproved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

Measuring the Technology Transition Performance by Data Envelopment Analysis

Panel #11: Analytics in Acquisition ManagementNaval Postgraduate SchoolApproved for public release; distribution is unlimited

Damages to return with a possible occurrence of eco-technology innovation measured by DEA environmental assessment

Abstract Environmental assessment and pollution protection are important concerns in modern business and policy. Consumers are interested in corporate efforts on not only their products but also environmental protections and pollution preventions. To attain a high level of sustainability (i.e., economic success and pollution prevention), all entities in private and public sectors need to pay attention to green technology innovation. This study considers that eco-technology innovation can combat the global warming and climate change, which the world is now facing as a major policy issue, along with its related business and policy supports. It is clear that any engineering and science efforts cannot attain any policy goal on the climate change without supports from social science perspectives, including business and economics. An important concern discussed in this study is how to identify an effective decision on eco-technology innovation and its influence on sustainability. To discuss the global issue at the world level, this study is first interested in measuring the performance of their operational and environmental achievements and then pays attention to Damages to Return (DTR). The economic concept indicates a level of change of undesirable outputs (e.g., CO2) by increasing one unit of a desirable output (e.g., oil production). To assess the magnitude of DTR, this study proposes a use of Data Envelopment Analysis (DEA). The proposed DEA assessment theoretically provides corporate leaders and policy makers with information regarding how to invest in eco-technology innovation for abatement of undesirable outputs, so enhancing the level of corporate or social sustainability

Operational Performance of Electric Power Firms: Comparison between Japan and South Korea by Non-Radial Measures

This study compares the electric power sectors between Japan and South (S) Korea. Both nations have been under a global trend of deregulation. To assess their progress due to industrial change and technology development, we use Data Envelopment Analysis (DEA) as an assessment tool that enables us to evaluate the level of simultaneous achievements on economic and technological measures, so assessing the degree of holistic development. DEA has been widely applied for performance assessment in the past decades. In this study, the method compares electric power firms by their operational efficiencies. To compare their achievements, it is necessary to develop a new type of DEA application for performance measurement. The proposed approach adds two analytical capabilities. First, the approach needs to handle “zero” in a data set and then restrict multipliers (i.e., weights among inputs and outputs) without any prior information to increase our empirical reliability. No study has simultaneously explored the two capabilities in DEA. Using the proposed method, our empirical study identifies two findings. One of the two is that the electric power industry of S. Korea outperformed that of the Japanese industry in the observed periods (2014–2018) because the Japanese power sector still suffered from an occurrence of the Fukushima Daiichi nuclear plant disaster which occurred on 1 March 2011. However, the difference has been gradually diminishing because the Japanese electricity industry has been gradually recovering from the huge disaster. The other is that the S. Korean power industry has been in a descending trend because the nation has shown technical regress as a result of inconsistent technology development (e.g., shifting its R&D: Research and Development) focus from electrical engineering to chemistry). The former R&D area is essential in maintaining the technical level of S. Korea′s electric power industry